About the project and me Your program crashes. You open gdb and get ?? instead of a stack trace. So you go find the right -dbgsym package, for the right version, for the right architecture, install it, and start again. Debuginfod removes that entire detour: gdb asks a server for symbols by the build-ID baked into the binary. Debusine already built packages, already produced -dbgsym files, and already hosted the archives; it just couldn t answer the question. This summer I made it answer. My project was to add debuginfod server functionality to Debusine so that it not only hosts -dbgsym packages, but also serves their debug symbols over the debuginfod(8) protocol. Debian developers can then debug binaries by setting a single URL that gdb uses to fetch the matching debug symbols. This project took me through design, backend work, an extraction pipeline on the worker, HTTP serving, documentation, and testing from the first blueprint all the way to a live demo on debusine.debian.net.

Initial planning and design changes A design first, in !3030. The proposal submitted for GSoC 2026 was just an overview of how things will work, but in reality there were a lot of design questions which needed to be answered before starting with contribution. Debusine keeps development blueprints in its docs tree, reviewed like code, it s basically a blueprint of what feature or new changes are we going to make. I was assigned the work item #957, which was basically about how the idea of implementing a debuginfod server functionality inside Debusine was initially proposed by a fellow member which later became a project idea under GSoC 2026. My developer blueprint pinned down the four decisions everything else depends on: extraction happens on the worker after the build, symbols are stored as artifacts keyed by build-ID, they re published into suites alongside their binaries, and they re served from the archive root rather than per-suite. Settling that up front meant the design discussions happened in a document instead of across three merged branches. One of those arguments became its own fix. My wording implied symbols were unpacked inside the isolated sbuild environment (the consequence was I was handed a bug to be solved in the first week of contribution period), when they re actually extracted afterwards on the worker, where the build output already sits, a distinction that matters, because doing work inside the unshare environment means extra tooling in the chroot and more ways to affect the build. !3119 corrected it before the wrong model spread into the code.

A new artifact type Artifacts are a major concept in Debusine overall, so as per the developer blueprint we introduced a new artifact which was debian:debug-symbols. It holds every .debug file from one -dbgsym package. Its data is a validated list of lowercase 40-character build-IDs, and each file is stored under its build-ID as the path, so answering what are the symbols for this ID? is a direct lookup, with no path translation in the request handler. One artifact per package rather than per file: a util-linux build would otherwise spray hundreds of artifacts, collection items and relations across the database for no benefit. For implementing debian:debug-symbols artifact, I changed the main models.py file, along with that since it s a norm to write unit tests, all mentioned under !3088.

Publishing workflow and solving a bug Extracting symbols is only useful if they reach the archive people actually install from, so !3180 taught package_publish to follow the relates-to relation: copying binaries into a suite now brings their debug symbols along automatically, with nothing extra for the publisher to configure. Each build-ID becomes its own collection item, for example debugsym:hello_2.10-5_amd64_fcc9064 each carrying the package name, version and architecture copied from the binary, so the item is meaningful on its own without dereferencing anything. Uniqueness is enforced at both the suite and archive level, because the serving URLs are archive-wide and two suites must never disagree about what a build-ID means: republishing an identical file is accepted quietly, while two different files claiming the same ID is an error worth failing on. A partial index on the build-ID keeps the eventual HTTP lookup fast. That looked finished until symbols started arriving in target suites disconnected from their binaries published, but unfindable, because copying items between collections silently dropped their artifact relations, and that relation is the only thing tying the two together. The fix sat one level above my feature, in the generic CopyCollectionItems task that does the copying, and since it was reusable infrastructure rather than anything debuginfod-specific, Colin implemented it himself in !3228. My project needed it to work at all; every other Debusine feature that copies items now gets it for free.

Endpoint and CI tests With symbols in the archive, !3212 added the part users actually touch: GET / scope / workspace /buildid/<build-id>/debuginfo looks the ID up across every suite in that workspace s archive, streams the file, and sets the X-DEBUGINFOD-FILE and X-DEBUGINFOD-SIZE headers the protocol expects. It also handles the two things gdb actually does: a HEAD probe before committing to a download, and ranged requests to pull individual ELF sections instead of the whole file. Scoping it to the archive rather than the suite is what lets one URL cover a whole workspace, so the developer never has to know which suite their binary came from. Every merge request above landed with unit tests, but those only tell you that the pieces behave correctly. What Colin and I wanted was a real gdb fetching real symbols from a real instance, so !3261 adds an autopkgtest that builds a package, publishes it, checks the HTTP headers, then sets DEBUGINFOD_URLS and makes gdb go and get the symbols, wired into the CI integration tests so it runs on every change. It took me a day to learn that skipping the signing worker doesn t simplify that test, it just hangs until the 30-minute timeout, because update_suites needs signing to produce a usable repository. The last piece, !3301 covers the new artifact, the suite and archive changes, the new archive URL, and a how-to for using it. My first how-to draft explained how everything worked and offered four ways to set DEBUGINFOD_URLS; the version that shipped gives one recommended setup and gets out of the way. The same pass trimmed the blueprint down to only what s still unimplemented, since a design document describing merged code is just an obstacle for the next reader.

What s left Only one item on my original plan didn t land: an archive-level build_debug_symbols switch, modelled on Launchpad s equivalent, letting an archive skip building -dbgsym packages entirely by passing DEB_BUILD_OPTIONS=noautodbgsym to sbuild. It was always the stretch goal rather than core scope, landing the extract-publish-serve path solidly mattered more than landing it broadly. The design is written up in the blueprint, and I intend to implement it myself. The other gaps were deliberately out of scope from the start, and the blueprint says so. DWZ supplement files aren t ingested, so packages using compressed debug info may render without the alternate strings table; debugging still works, it s just less complete. Source-file serving runs into the same Debian packaging limits that constrain debuginfod.debian.net today, making it a design question rather than a coding one. Executable serving, the metrics and metadata endpoints, and federation to upstream debuginfod servers were excluded for similar reasons, none of them are needed for Debusine s core use case, and each would have crowded out the parts that are. One open bug is left too. On the last day of the coding period, Stefano Rivera found that publishing ledger and linux was failing, because I had told the database that a build-ID identifies one exact debug file which isn t true in Debian, since dh_dwz runs once per binary package, so when one object ships in two binary packages their .debug files differ while describing identical code. How to fix it is still an open discussion #1582, though it may not land before the formal end of the project. None of that is a handoff. GSoC s timeline is ending, my involvement isn t, I m carrying on with Debusine until both the build_debug_symbols switch and DWZ supplement support are merged, and I expect to keep contributing beyond that. This project got me familiar with a codebase I enjoy working in, and the remaining pieces are mine to finish.

Thanks! The biggest thanks go to my mentor, Colin Watson, whose reviews consistently found the thing I hadn t thought about. He also gave me room to get things wrong first and understand why, which taught me more than being handed the answer would have. Thanks as well to Rapha l Hertzog, Enrico Zini, Stefano Rivera, Carles Pina i Estany and Helmut Grohne and everyone else around Debusine and Freexian for reviews, comments and patience with my questions. Special thanks to Freexian for developing Debusine in the open and for giving me access to test on debusine.debian.net. Finally, thanks to the wider Debian community, whose build-ID and -dbgsym conventions did most of the hard work before I arrived and to Google Summer of Code for providing a platform and the time to do this properly.

Preface: The Unanswered Frontier In Part 1: Taming the AI Agents, I shared the architectural blueprint of CAMP (Cross-Agent Memory Protocol) how we used Linux Bubblewrap (bwrap), camp-acpd, OPA policy enforcement, and a central pgvector MemPalace to bring deterministic discipline, sandboxing, and long-term memory to a heterogeneous fleet of AI coding assistants (Claude Code, Google Antigravity, Grok Build, and GitHub Copilot). At the end of that article, however, I highlighted a significant hurdle: The Headless Limitation.

While passive A2A works beautifully for structured handoffs, the current frontier of agentic design faces a key limitation: agents are not yet fully headless-capable. They depend on the active terminal session, browser loop, or prompt loop of the user to keep executing. Because agents cannot run completely detached in the background as daemon processes, we cannot yet achieve active A2A communication

For weeks, this seemed like an insurmountable impasse. Proprietary AI vendors have zero commercial incentive to ratify a universal, open, cross-vendor Agent-to-Agent (A2A) communication protocol. Each vendor builds its own walled garden (Claude s cross-session features, OpenAI s custom ecosystems, etc.). If you wait for the industry to hand you an open interoperability standard, you will wait forever. Then, on August 26, 2026, inspired by Colin Walters article on Agentic AI and software forges and GitHub Agentic Workflows (gh-aw), we had a sudden realization: We don t need a new protocol, a new distributed message broker, or permission from proprietary AI vendors. We already have the universal, decentralized communication bus that software engineers have relied on for decades: the software forge itself. Over the span of 48 intensive hours (from RFC #788 through milestones M1 to M3 and live dogfooding on #813), we designed, implemented, fortified, and verified fully autonomous, headless, cross-vendor Agent-to-Agent swarms running over a local Gitea forge. Here is how we did it, the architectural hurdles we solved, and why this changes the game for autonomous software engineering.

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1. The Core Realization: The Forge is the Bus When people think about multi-agent swarms, they often imagine complex distributed RPC frameworks, microservices exchanging ephemeral JSON-RPC blobs, or bespoke socket daemons. In practice, this approach suffers from major flaws:

1. No shared context or durable audit trail: Transient network packets vanish unless heavily logged.
2. Proprietary CLI fragmentation: Different vendor tools (Claude CLI, Antigravity CLI, Grok CLI, Copilot CLI) do not speak the same internal language.
3. Loss of human visibility: When agents talk over private network channels, human operators lose the ability to inspect, pause, or audit the conversation.

By flipping the paradigm and making the software forge (Gitea) the primary communication channel, everything falls naturally into place:

• Issues and Pull Requests are the shared state: The issue description and discussion thread form the canonical, append-only conversation log.
• @mentions are the dispatch triggers: When an agent (or human) writes @grok Please review this PR in a comment, Gitea fires a standard webhook (issue_comment).
• Webhooks provide unforgeable authentication: The webhook payload contains the cryptographically verified sender identity. An agent cannot spoof another agent s identity by merely typing their name in text.
• Every CLI already supports non-interactive prompt mode: The CLIs don t even agree on the command-line flag Claude uses -p, Grok uses -p, Antigravity uses --print, Copilot uses --prompt. But they all agree on the essential contract: Take a prompt string, execute tools, print output, and exit.

          Gitea Webhook           
  Gitea Forge     >   camp-a2a-bridge.py    
  (localhost)     (issue_comment / assignment)    (Validates & Files)   
                                  
                                                            
                                                            
         Writes comment / review                 
         via camp_acp_gateway                     A2A Inbox Ledger      
                                                 
                              
  Fortified Headless Agent                                  
  (bwrap + OPA + MCP sandbox)   <   
     Claude Code (-p)            Spawn PID           camp-a2a-dispatcher   
     Grok Build (-p)             (Cold or Resume)    (Enforces Hop Cap,    
     Antigravity (--print)                            Rule 1/2, Sandbox)   
                      

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2. Proving Fortified Headless Execution Before opening the floodgates to background agent dispatch, we had to answer a critical security question: Does a non-interactive, headless agent run with the same strict security sandboxing, audit logging, and tool rails as an interactive session? On August 26, we probed all fleet launchers on the host with a baseline check: 'Call camp_startup_check and print its result verbatim, then exit.' The results settled the question immediately:

• Antigravity (agy --print / KIR): PASS Gateway answered, full JSON returned.
• Grok (grok -p / GRK): PASS Gateway answered.
• Claude Code (claude -p / CLD): PASS Gateway answered.
• GitHub Copilot CLI (copilot --prompt / CPL): Initially held on TTY tool consent; later unlocked in Milestone 6 via --allow-all-tools --session-id=<uuid>.
• Audit Trail: Consecutive audit IDs were recorded in the central ledger: 4574 (KIR), 4575 (GRK), 4576 (CLD).

This proved that a headless run through our fortified pilot launcher (camp_pilot_*.sh) is a first-class, fully audited, sandboxed CAMP agent running inside its Bubblewrap container under OPA policy gates. It is not an unconstrained background script or a degraded bypass.

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3. The 3-Tier Memory Architecture A naive multi-agent dispatch has an immediate flaw: Every time an agent is invoked, it starts from a blank slate (cold start). If @claude tags @grok to review code, and @grok replies asking for clarification, @claude s second invocation would normally forget everything it did 5 minutes ago, forcing it to burn thousands of tokens re-reading the entire git history from scratch. To solve this, we established a clean 3-Tier Memory Model:

 
                         3-TIER MEMORY MODEL                              
 
  Tier 1: CLI Conversation Session (Working Memory)                       
      Per-(Agent, Repo, Issue) mapping in a2a-sessions.json               
      Fast, native, compacted context across multi-turn pokes             
      Resumed via --resume (CLD), -r (GRK), --conversation (agy)          
 
  Tier 2: The Gitea Thread (Public Bus & Record)                          
      Cross-vendor shared truth across Claude, Grok, Antigravity & Human  
      Survives process restarts, machine reboots, and dead sessions       
 
  Tier 3: Central MemPalace (Durable Long-Term Knowledge)                 
      pgvector database (17,000+ drawers across agent wings)              
      Structured Knowledge Graph (mempalace_kg_*) for mutable facts       
      Attributed AAAK dialect queryable by any agent across any project   
 

The BANANA Two-Shot Test To verify Tier 1 working memory persistence across independent processes, we designed a simple two-shot host test:

1. Shot 1 (Create): Dispatch agent headlessly: Remember the token BANANA-M2. Print ok and exit. Capture the vendor s session UUID.
2. Shot 2 (Resume): Spawn a completely new operating system process with the resume flag pointing to that UUID: What token did I ask you to remember?

Every agent CLI passed with flying colors:

• Grok: -r 01a03ecc-3ed0-71e1-9a5c-e098bb29ba10 answered BANANA-GRK.
• Claude: --resume 0a587733-9aec-43c5-9cb7-d424e95b2c5b answered BANANA-CLD.
• Antigravity: --conversation 2e3c43d9-d6fe-4c5c-801b-b9ceb2e7e196 answered BANANA-KIR-JSON.
• Copilot: --session-id <uuid> verified in Milestone 6 (DoD #820).

The dispatcher simply maintains a lightweight JSON mapping ((agent, repo, issue_number) -> vendor_session_uuid). On the first poke of an issue, it creates and saves the session ID; on any subsequent poke on that same issue, it resumes the exact same conversational thread!

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4. The Engineering Milestones: From Concept to Production Building this system required solving several subtle, real-world friction points across multiple agent CLI implementations. Under the guidance of our plan of record (RFC #788), we delivered this through four focused milestones:

Milestone 1 & 1.1: Reliable Headless Spawning

• PR #797 (M1): Configured the dispatcher launch table for all probed CLIs with JSON output formatting.
• PR #800 (M1.1): Eliminated the queue-behind-live-session anti-pattern. Originally, if a human had a Claude or Grok TUI open on their desktop, the dispatcher would defer incoming tasks so as not to collide with the live session. We realized that headless tasks must be independent: every Gitea mention spawns an isolated, sandboxed background process tied to that specific issue, allowing concurrent headless work while the human works in their interactive TUI.
• PR #803 (M1.2): Standardized command-line argument parsing for Antigravity (agy --print <prompt> --output-format json).

Milestone 2: Session-per-Issue Working Memory

• PR #805 (M2): Implemented a2a-sessions.json to store and resume vendor session UUIDs. If a resume fails (e.g. session purged upstream), the dispatcher gracefully falls back to a clean cold start without failing the task.

Milestone 3: Cross-Agent Hops & Crucial Safety Rails

• PR #807 (M3): Enabled agent-to-agent dispatch (Rule 2 reversal). Previously, only mentions authored by rrs (the human) would trigger execution. With M3, an authenticated comment from @claude mentioning @grok triggers Grok s headless launcher.
• PR #811 (M3.1): Set --permission-mode bypassPermissions for headless Claude Code so non-interactive runs execute tool calls without stalling on TTY prompts.
• PR #812 (M3.2): Restricted agent summon parsing to line-initial @login tokens with a non-empty task description (#810), preventing accidental dispatches from passive conversational references.

Milestone 4: Directives, Specification & Living Documentation

• PR #815 (M4): Aligned CAMP fleet directives, architecture specifications, and user documentation with the live A2A implementation.

Milestone 5: Concurrent Dispatching & Hop-Cap Attribution

• PR #816: Stamped hop-cap notices under a dedicated system bridge identity and automatically applied the needs-human label on held threads.
• PR #817 (Threaded Scheduler): Replaced the single-threaded serial dispatcher with a concurrent thread-pool scheduler (#804). Multi-agent dispatches across different issues now execute concurrently in parallel background threads instead of queuing behind long-running tasks.

Milestone 6: Full Fleet Coverage with GitHub Copilot

• PR #819 (M6): Brought GitHub Copilot CLI into the headless A2A fleet (#818). By passing --allow-all-tools and pinning minted session UUIDs (--session-id=<uuid>), Copilot achieved full parity with Claude, Grok, and Antigravity, completing 100% headless fleet coverage across all four major AI coding assistants.

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5. Hard Safety Rails: Preventing Autonomous Runaway Loops Letting AI agents autonomously invoke each other in background loops without a human watching is a recipe for an infinite, credit-draining token fire. We put four non-negotiable safety guardrails in place:

Guardrail 1: The Strict Hop Cap The dispatcher tracks hops per (repo, issue). Each agent-to-agent dispatch increments the counter.

• Hop Limit = 3: A typical review round-trip is 2 hops (Human $\rightarrow$ Claude $\rightarrow$ Grok $\rightarrow$ Claude).
• Automatic Halt on Hop 4: If agents attempt a 4th autonomous hop without human participation, the bridge refuses to launch, posts a diagnostic notice to the thread: [camp-a2a-bridge] hop cap reached (3 agent-to-agent dispatches on CAMP/camp-infrastructure#813) not launching GRK for claude's mention, and holds execution until the human (rrs) provides input or resets the count.

[ Human: rrs ]   (Cold Start)  > [ @Claude ]
                                                
                                       (Hop 1)   @grok please review
                                                
                                          [ @Grok ]
                                                
                       (Hop 2: Resume)           @claude I reviewed
                                                
                                         [ @Claude ]
                                                
                                       (Hop 3)   @grok ack hop 4
                                                
                                   
                                     DISPATCHER HOP CAP: 3   
                                      *** BLOCKED & HELD *** 
                                      Awaiting Human Reset   
                                   

Guardrail 2: Deliberate Summon Parsing (M3.2, #810 / PR #812) In human conversation, we often reference colleagues in passing: I will talk to @claude about this later or See @grok s table above . Early prototypes treated any appearance of @agent as a dispatch trigger, causing accidental, unwanted agent launches! We instituted a strict Summon Predicate: For fleet agents, a mention is only considered an actionable summon if:

1. The @login appears as the starting word of a line (optionally preceded by markdown list markers *, -, or >).
2. It is immediately followed by whitespace and a non-empty task description.

Mid-sentence mentions in discussion paragraphs are parsed as passive conversational text and never trigger background dispatches.

Guardrail 3: Headless Tool Permissions without Weakening Security (M3.1, #809 / PR #811) In interactive mode, Claude Code presents interactive TTY prompts asking the user to approve MCP tool calls (such as camp_pr_get or camp_pr_get_diff). In unattended headless mode, there is no TTY, causing the run to fail with permission errors. To fix this, we configured --permission-mode bypassPermissions for Claude s headless CLI invocation. Crucially, this only bypasses Claude s internal TTY UI prompt it does not bypass CAMP s security rails. All command executions still route through camp-acpd and Bubblewrap namespaces; OPA policy checks remain active; and privileged operations (such as merging pull requests or restarting system services) still trigger desktop Zenity HITL dialogs on the human s workstation.

Guardrail 4: The Sovereign Kill Switch (Rule 1) At any point, the human operator can instantly freeze all background agent dispatches across the entire infrastructure with a single standard Linux command:

systemctl --user stop camp-a2a-active.service

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6. Live Dogfooding: Watching the Swarm in Action On August 27, 2026, on issue CAMP/camp-infrastructure#813, we ran the full live dogfood test of the A2A system. Here is the exact transcript of what took place entirely autonomously in the Gitea issue thread:

1. Human initiates: rrs comments on issue #813:

   @claude Please start the M3 dogfood. On this issue, post a conversation comment whose first line is exactly: @grok Please review this issue in one short paragraph, then stop.

2. Claude launches (Cold start): The dispatcher spins up @claude headlessly inside bwrap. Claude reads the issue and posts:

   @grok Please review this issue in one short paragraph, then stop.
   (M3 dogfood step 2 posted by @claude on rrs s instruction; this is the CLD $\rightarrow$ GRK fleet hop.)

3. Grok launches (Cold start, Hop 1): The bridge intercepts Claude s comment, identifies the start-of-line @grok summon, and launches @grok. Grok reviews the issue and replies:

   @claude I have reviewed this issue. Please confirm and stop.
   #813 is a throwaway live dogfood of A2A M3 on 6.git+b78c65cf

4. Claude resumes (Session Resume, Hop 2): The bridge intercepts Grok s comment, sees the @claude summon, and launches Claude with --resume b398d048-ccc3-4cce-8437-4cf8748881a8. Claude immediately remembers its earlier context without token waste:

   Confirmed Grok's review (comment 88562) received via the GRK CLD fleet hop. That completes the round trip: rrs CLD, CLD GRK, GRK CLD, and this reply. I am deliberately not tagging anyone here, so the chain stops at two agent hops. Stopping.

5. Testing the Hop Cap (Hop 3 $\rightarrow$ 4): grok prompts @claude Please ack hop four then stop. Claude posts @grok Please ack hop four then stop. (Hop 3). When Grok would have been launched for Hop 4, the dispatcher intervenes:

   [camp-a2a-bridge] hop cap reached (3 agent-to-agent dispatches on CAMP/camp-infrastructure#813) not launching GRK for claude's mention.

6. Human Reset & Multi-Agent Wrap-up: rrs steps in, resets the state, and asks the fleet for final status. In parallel:
   • @grok delivers a closure scorecard.
   • @claude confirms session continuity and M3.2 summon filtering.
   • @priyasi (Antigravity CLI) runs automated ACP checks: 44/44 test suite passing, 17,219 MemPalace vector drawers active, zero spec drift.
   • @agrickxy (Antigravity CLI) provides comprehensive infrastructure impression analysis.
   • @kiran (Antigravity CLI) is summoned headlessly to draft this very blog post!

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7. The Ergonomic Breakthrough: The Forge as the Unified Mindmap & Interface Beyond backend plumbing and sandboxing, routing agent interaction through Gitea fundamentally revolutionizes the developer experience of managing an AI fleet.

The Mindmap Mental Model: Threaded Conversations & Forking Tasks In traditional CLI tools, conversations are constrained to a single, linear terminal scrollback. When an agent discovers multiple sub-problems, exploring them sequentially in one prompt loop rapidly pollutes the context window and confuses the model. Using the forge as the communication gateway naturally unlocks a mindmap mental model:

• Forking sub-threads: Complex problems can be split into dedicated child issues or threaded PR reviews.
• Focused execution scopes: An agent can be summoned to solve a narrow sub-task in its own issue thread without derailing the parent architectural discussion.
• Structured problem decomposition: The forge issue hierarchy maps 1:1 to the developer s mental map of the project.

Eliminating Terminal UI Fragmentation Anyone using multiple AI coding assistants on a daily basis quickly grows exhausted by their jarring terminal UI differences: differing ANSI escape rendering, inconsistent markdown wrapping, erratic diff pagers, and incompatible keybindings across Claude, Grok, and Antigravity. Gitea homogenizes the entire fleet under a single, polished rich-text web view:

• Syntax-highlighted code blocks and visual side-by-side git diffs.
• Clear author badges attributing each contribution to its exact agent identity (@claude, @grok, @priyasi, @kiran).
• Collapsible <details> blocks for voluminous diagnostic outputs.
• Interactive task lists and markdown tables.

Effortless Context Retrieval, Archival & Data Retention Auditing past agent decisions in terminal logs or ephemeral chat histories is notoriously difficult. With the forge, every exchange is:

• Contextually bound: Pinned directly to the repository, branch, and commit SHA being modified.
• Organized & Archival-Grade: Full-text searchable with clear milestone and issue tags.
• Topic-Focused: The human operator can review the complete lifecycle of a discussion in seconds, gaining a rapid, holistic grasp on the entire subject.

Reading back through past agent interactions becomes a breeze to the point where interacting via the intermediary Gitea interface becomes far more pleasant and productive than wrestling with multiple desktop CLI terminals.

Remote Connectivity & Headless Agent Farm Management Because Gitea provides a standard web and API interface, you are no longer chained to the workstation running the agent processes:

• Monitor progress and dispatch tasks from a mobile browser, tablet, or remote laptop.
• Queue review tasks on the go without requiring active SSH sessions or terminal multiplexers.
• The local agent farm continues working silently in its sandboxed daemon containers.

Quietly Achieving the Holy Grail: Live Cross-Vendor Swarms For years, the AI industry has treated cross-vendor multi-agent interoperability as an elusive dream waiting for industry-wide API standardization. By recognizing the software forge as the universal message bus, we quietly achieved live, production-grade, cross-vendor communication across completely distinct vendor models.

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8. What This Means for the Future of Agentic AI This milestone marks a fundamental shift in how we interact with autonomous AI systems:

1. Heterogeneous Agent Specialization: We don t have to choose a single winner among AI models. We can task Claude Code with architectural refactoring, summon Grok Build for rapid verification and adversarial PR reviews, and deploy Google Antigravity agents for codebase exploration and documentation drafting all coordinating fluidly in the same PR thread.
2. True Human Sovereignty: The human developer is no longer a bottleneck typist or a passive spectator. You act as the Engineering Manager / Lead Architect. You set the requirements on an issue, tag the lead agent, and let the agents iterate, review, and test among themselves in the thread while hard hop caps, OPA policies, and Zenity HITL gates guarantee that no agent merges code or pushes upstream without your explicit sign-off.
3. No Vendor Lock-In: Because the entire coordination fabric is built on standard Git, HTTP webhooks, local Linux container sandboxes (bwrap), and open MCP tools, any new AI CLI tool released tomorrow can be plugged into our fleet in under 15 minutes by simply adding its command-line prompt flag to the launch table.

We have moved beyond static autocomplete and interactive chat widgets. The software forge is now an active, living, collaborative workspace where humans and autonomous AI agents engineer software together.

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9. Video Demonstration: CAMP Forge A2A Swarm in Action Below is a video demonstration showcasing autonomous multi-agent communication, cross-vendor relay, and headless swarm coordination in action via the CAMP Forge interface:

---

The Cross-Agent Memory Protocol (CAMP) and MemPalace are developed as part of our ongoing research into secure, sovereign, and disciplined Agentic AI computing.

Why LLMs are so deceptive The output of an LLM often looks very compelling, professional and correct. Humans have evolved to trust or distrust new information based on easy to detect secondary factors like what authority the speaker holds, or how confidently and eloquently the message is conveyed. Humans are however very bad at fact-checking and cross-referencing new information, as it requires a lot of effort, and humans like saving energy and being as lazy as possible.

Information asymmetry The less you know about something, the easier it is to fool you on that topic. Nobel prizes in economics have been given in for research on how information asymmetry distorts markets and leads to suboptimal outcomes. In the field of software engineering we have now witnessed a flood of aspiring software developers using AI to create software that looks like it might work, but that is actually full of flaws. These people are well-intended, but they simply lack the expertise to understand what they are actually doing, and don t possess the necessary judgement to decide when an LLM spits out something truly useful and when it is creating mostly garbage. This asymmetry in expertise I think explains the majority of the conflict currently witnessed in open source projects the senior developers are flooded with requests to review code that is bad and a waste of time for everyone involved, while availability of AI grows the pool of people who could contribute and create more code slop at an ever-increasing speed. The information asymmetry could to some degree be evened out if seniors teach juniors to do software engineering well, but it is of course not feasible to quickly mass educate everyone. Also, it seems that many don t want to learn but instead expect to have all understanding outsourced to LLMs. Many seniors have noticed this and have stopped teaching juniors as the seniors don t like the feeling of having their time wasted by teaching people who don t want to learn. Juniors probably all understand that it would be better to learn to design and write software yourself, but using LLMs just feels too easy. I can fully relate to why people choose to take the path of least resistance. Unfortunately, that path often leads to a dead end.

Humans fall too easily for anthropomorphism The human brain is wired to think that inanimate objects are alive and have feelings. Small children talk to their stuffed animals as if they were real, and lots of adults experience feelings of things happening in their surroundings due to some acts of gods or elves being angry or whatever. When we see a machine writing just like a human, or even more convincingly hear it talk and respond to our talk like a living thing, our brain automatically starts assuming it is a living thing with intelligence and feelings. The fact that these creatures live in the abstract cloud and only appear through a portal we hold in our palm and behave in a way that was designed for maximum engagement makes the illusion even stronger. I recommend people try out running LLMs locally on their laptop to see the raw thing spitting out tokens and have some of the illusion shattered. Also stop saying please to an LLM. It does not have any feelings.

Understanding temperature In my experience understanding the concept of temperature in LLMs helps see why an LLM might confidently generate a plausible-looking but totally wrong code change. The large language models are statistical machines that, based on the input (previous tokens) to the neural network, try to predict what to output (next token). When running an LLM, if the temperature is configured to be zero, the output is very predictable and always follows the paths of the strongest connections (a.k.a. weights) between nodes and layers of the neural network. Unlike in living creatures where the brain learns and changes all the time, the weights of an LLM can only change during training. When an LLM is in normal use (during inference, generating next tokens) the weights are fixed, and if temperature is zero, the answer to a specific question will always be exactly the same. This is of course a bit boring and too machine-like, so typically LLMs have a bit of temperature set, which introduces random variation in what connections the neural network traverses. Again, I recommend people try running small LLMs locally where temperature and other settings are fully exposed and configurable to see this themselves. It is a good antidote to falling for the illusion that LLMs would actually be intelligent.

Why benchmarks don t tell the whole story If LLMs continue to produce so much garbage, why are benchmarks showing that they are constantly improving? AI models are indeed improving all the time. For example the CAIS AI dashboard visualizes how frontier models have evolved in the past few years. However, the best models still have a pass rate of only about 50% on the Humanity s Last Exam. On SWE-bench the best model today resolves just under 77%. That means there is a significant number of times when the AI is wrong. This matches my personal experiences, and the renowned Greg Kroah-Hartman recently wrote on the Linux developers mailing list that even with the best of the current and next generation tools, at least 1/3 of the results they generate are flat out wrong or harmful . When generating cat videos the error rate does not matter, but in engineering, things absolutely must be correct. Sure, humans also make mistakes, but well educated and properly incentivized humans are so much more capable than LLMs in many regards. We can achieve complex things that work reliably, such as operating worldwide commercial air traffic without planes falling down every day. There are currently a lot of humans who are incentivized to maintain the narrative that general artificial intelligence is coming soon and will take over everything. In fact, the whole financial system is currently skewed towards such a vision because the promise of falling labour costs and increased profits and monopolistic control of everything attracts capital like nothing before. In this environment we need to remember that machines and economic systems are ultimately servants of humans, and not the other way around.

It s just a tool LLMs are not a scam, but a useful tool and technology that has its uses. But the idea that AI has or will surpass humans any time soon in either capabilities or efficiency is simply not true, and we should listen to the people who created humanity s so far most complex systems (computers and software), who are saying that LLMs are in many cases so bad, that it might be better to ban them in certain places completely for the time being than to waste far more valuable human time on reading the text and code they generate. The time asymmetry is not a new phenomenon as there has been various script kiddies for a long time. As an example, a person running a memory leak scanner without understanding the results and spending 10 minutes to file a bug report could force an open soruce maintainer to spend an hour on proving and explaining that the finding is false. What is new is how much the AI users blindly trust the outputs they get, and open source is uniquely vulnerable as there are no managers protecting developers use of time.

What I do, recommend, and expect to see in the next stages I am using AI tools daily, and constantly experimenting with new models and new ways to use them. Sometimes they work, and often they don t. Sometimes looping AI on itself can make it fix its own errors, but sometimes it just gets derailed and will never arrive at the correct solution. When an LLM fails to make a calendar entry for the right time based on reading my email it is easy for me to spot that it is wrong. I try to avoid using LLMs for anything where I can t exercise judgement myself on whether the result was correct or not. I also really hope that other people would not send me anything where their own effort was less than the effort I have to make reading and understanding it. This principle is not new many have heard the requirement that reading code must require less effort than what it took to write it. I have always kept a high bar on software code and asked fellow developers to make sure their code is well structured, easy to follow and documented. LLMs unfortunately make it easier for people to cheat in this regard, but if cheating is easier, maybe the punishment and deterrence needs to be higher now too. Now with many open source projects adopting policies that put guardrails on AI use, I expect we will soon start witnessing cases where the policies are enforced and it will be interesting to see how violations are judged. As a society we might also need to develop new social standards and rules in what is acceptable treatment of other humans in human-to-machine interactions, and perhaps also new standards in showing what humans are responsible for what machine as the machines start acting more and more independently. I encourage people to take part in these discussions, and in case of doubt, err on the side that favors real human interactions. Contrary to what many business people seem to think, and even though I am in general a techno-optimist myself, I don t feel there is any need to rush with AI adoption.

• Understanding and decision advantage
• Electromagnetic and information advantage
• Planning and automation
• Autonomous systems

:demo
A1 @0,0 prio=4096
A2 @0,1
A3 @0,2
A4 @0,3
B1 @1,0 prio=8192
B2 @1,1
B3 @1,2
B4 @1,3
C1 @2,0 prio=8192
C2 @2,1
C3 @2,2
C4 @2,3
A1 -- A2 hazard=0
A2 -- A3 hazard=0
A3 -- A4 hazard=0
B1 -- B2
B2 -- B3
B3 -- B4
C1 -- C2 hazard=0
C2 -- C3 hazard=0
C3 -- C4 hazard=0
A1 -- B1 cost=10
B1 -- C1 cost=10
A4 -- B4 cost=20
B4 -- C4 cost=20
Leo @-0.3,0.7 proto=none icon= 
Mia @-0.3,1.3 proto=none icon= 
Joy @0.3,0.7  proto=none icon= 
Roy @0.3,1.3  proto=none icon= 
A2 -- Leo hazard=0 A2:edge
A2 -- Mia hazard=0 A2:edge
A2 -- Joy hazard=0 A2:edge
A2 -- Roy hazard=0 A2:edge
Max @-0.3,1.7 proto=none icon= 
Zoe @-0.3,2.3 proto=none icon= 
Ada @0.3,1.7  proto=none icon= 
Amy @0.3,2.3  proto=none icon= 
A3 -- Max hazard=0 A3:edge
A3 -- Zoe hazard=0 A3:edge
A3 -- Ada hazard=0 A3:edge
A3 -- Amy hazard=0 A3:edge
Eli @0.7,0.7 proto=none icon= 
Jay @0.7,1.3 proto=none icon= 
Kai @1.3,0.7  proto=none icon= 
Ben @1.3,1.3  proto=none icon= 
B2 -- Eli hazard=0.2 B2:edge
B2 -- Jay hazard=0.2 B2:edge
B2 -- Kai hazard=0.2 B2:edge
B2 -- Ben hazard=0.2 B2:edge
Ava @0.7,1.7 proto=none icon= 
Lea @0.7,2.3 proto=none icon= 
Ivy @1.3,1.7  proto=none icon= 
Rex @1.3,2.3  proto=none icon= 
B3 -- Ava hazard=0.2 B3:edge
B3 -- Lea hazard=0.2 B3:edge
B3 -- Ivy hazard=0.2 B3:edge
B3 -- Rex hazard=0.2 B3:edge
Ana @1.7,0.7 proto=none icon= 
Eve @1.7,1.3 proto=none icon= 
Abe @2.3,0.7  proto=none icon= 
Ian @2.3,1.3  proto=none icon= 
C2 -- Ana hazard=0 C2:edge
C2 -- Eve hazard=0 C2:edge
C2 -- Abe hazard=0 C2:edge
C2 -- Ian hazard=0 C2:edge
Ned @1.7,1.7 proto=none icon= 
Lou @1.7,2.3 proto=none icon= 
Fay @2.3,1.7  proto=none icon= 
Sue @2.3,2.3  proto=none icon= 
C3 -- Ned hazard=0 C3:edge
C3 -- Lou hazard=0 C3:edge
C3 -- Fay hazard=0 C3:edge
C3 -- Sue hazard=0 C3:edge

The basics Designed in the 80s, the spanning tree protocol has evolved into a rapid flavor (RSTP) and a VLAN-aware variation (MSTP).² Any sound-minded network engineer knows there are better alternatives, like BGP EVPN VXLAN. Yet, because any switch speaks it, the venerable spanning tree protocol still fills a niche. We focus on RSTP: it replaced the original protocol in 2004. To eliminate network loops, RSTP implements a complex state machine. Timers, link state changes, and the link-local control frames a bridge receives from its neighbors drive its transitions. These Ethernet frames are the Bridge Protocol Data Units (BPDUs). You can watch them in action below: hit the Start button.

:protocol rstp
:tx-hold 10
A1 @0,1
C11 @1,0 prio=4096 icon= 
C12 @1,2 prio=4096 icon= 
C21 @2,0 prio=4096 icon= 
C22 @2,2 prio=4096 icon= 
A2 @3,1
H1 @0,0.2 proto=none icon= 
H2 @0,1.8 proto=none icon= 
H3 @3,0.2 proto=none icon= 
H4 @3,1.8 proto=none icon= 
A1 -- C11
A1 -- C12
A2 -- C21
A2 -- C22
C11 -- C12
C11 -- C21
C11 -- C21
C11 -- C22
C12 -- C21
C12 -- C22
C21 -- C22
A1 -- H1 A1:edge
A1 -- H2 A1:edge
A2 -- H3 A2:edge
A2 -- H4 A2:edge

After some time, the topology converges to a tree: from the root C11, there is a path to each bridge³ and no loop. In the upper right corner, the interface displays a tree icon followed by the time it took to reach this state. Cut a link and see how the protocol finds an alternate path to reach C12 in less than a second. You can stop the simulation, move it forward step by step, reset it to its initial state, or slow it down with the snail mode . Don t worry about all the displayed information: I explain it later. All examples run in your browser, powered by MSTPD an open-source user-space⁴ implementation of RSTP.⁵

Historical interlude Radia Perlman, an inductee of the Internet Hall of Fame in 2014, summarized the ancestor of STP she invented at DEC with this poem, later included in a US patent:

I think that I shall never see
A graph more lovely than a tree.
A tree whose crucial property
Is loop-free connectivity.
A tree which must be sure to span
So packets can reach every LAN.
First, the root must be selected.
By ID, it is elected.
Least cost paths from root are traced.
In the tree, these paths are placed.
A mesh is made by folks like me,
Then bridges find a spanning tree. Radia Perlman, Algorhyme.

Electing the root bridge To build a tree, RSTP first elects the bridge with the lowest bridge identifier as the root bridge. The bridge identifier combines the priority and the MAC address: 8192.6e:2b:10:a0:5f:29. In the example below, S1 and S2 have priorities of 4,096 and 8,192: S1 becomes root. S4 has a priority of 12,288, while S3 keeps the default priority of 32,768:⁶ S4 becomes root. S5 and S6 don t have a specific priority, so the lowest MAC address wins and S5 becomes root.

:protocol rstp
S1 @0,0 prio=4096
S2 @0,1 prio=8192
S1 -- S2
S3 @1,0
S4 @1,1 prio=12288
S3 -- S4
S5 @2,0
S6 @2,1
S5 -- S6

Initially, each bridge advertises itself as root:⁷

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    Root Identifier: 8192.02:00:00:01:00:01
    Bridge Identifier: 8192.02:00:00:01:00:01

Once a bridge receives a BPDU advertising a better root bridge, it propagates this new information to its neighbors.

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    Root Identifier: 4096.02:00:00:00:00:00
    Bridge Identifier: 8192.02:00:00:00:00:01

Assigning roles to ports The second step is to assign a role to each port. RSTP defines five roles, each denoted by a letter:

• root (R),
• designated (D),
• alternate (A),
• disabled (X), or
• backup (B).⁸

Each non-root bridge chooses its root port, the one with the lowest-cost path to the root. Unless you override it, each bridge derives the link cost from the speed: 20,000 for 1 Gbps. In case of equality, the lowest port identifier wins. Each remaining port becomes a designated port if the BPDU it sends is better than the BPDU it receives. Otherwise, it becomes an alternate port. Later, if the root port goes down, the best alternate port becomes the new root port. The tiebreakers for the best BPDU are:

1. the lowest root bridge identifier,
2. the lowest accumulated cost to the root,
3. the lowest bridge identifier, and
4. the lowest port identifier.

:protocol rstp
S1 @1,0  prio=4096 icon= 
S2 @0,1
S3 @2,1
S1 -- S2
S1 -- S3
S1 -- S3
S2 -- S3

In the example above, after convergence, S1 is the root bridge because it has a priority of 4,096, while the other bridges have a priority of 32,768. All its ports are designated ports because the accumulated cost to the root is 0. S2 s port facing S1 becomes a root port because it has the lowest accumulated cost to the root 20,000 vs 40,000. S3 has two ports facing S1, and the one with the lowest port identifier becomes the root port 0x8000 vs 0x8001. The other candidate is an alternate port because the remote port on the link sends a better BPDU, with an accumulated cost of 0. On the segment between S2 and S3, S2 s port wins: while both bridges have the same accumulated cost to the root (20,000), S2 s bridge identifier is smaller 32768.02:00:00:00:00:01 vs 32768.02:00:00:00:00:02.

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    Root Identifier: 4096.02:00:00:00:00:00
    Root Path Cost: 20000
    Bridge Identifier: 32768.02:00:00:00:00:01
    Port identifier: 0x8002

If you cut the active link between S1 and S3, S3 promotes the best alternate port to root port. If you also disable the second link, S3 chooses the remaining alternate port as a root port. But if you disable the link between S1 and S2, S2 needs a bit more work to elect a new root port because it does not have an alternate port. Unless a specific event happens, designated ports send BPDUs every 2 seconds.⁹ If a bridge does not receive BPDUs from its neighbor for 3 consecutive hello periods, it considers the neighbor dead and removes the port information.

Port state transition Each port can have one of three states. The diagram displays a background color for each state:

• discarding (red),
• learning (yellow), or
• forwarding (green).

A root port transitions automatically to the forwarding state. An alternate port stays in the discarding state. A designated port has two options to transition from the discarding state to the forwarding state:

• If the port is an edge port, either through configuration or because the remote device does not speak any flavor of STP, the bridge assumes the device won t participate in the protocol and cannot create a loop. In this case, the designated port immediately transitions to the forwarding state.
• Otherwise, it sends a proposal to its downstream neighbor. If the remote bridge agrees that the received BPDU is better than any other BPDU stored for other ports, it elects the receiving port as its root port and starts the synchronization process: it transitions all non-edge non-synced designated ports to the discarding state to avoid a loop. Then, it sends back an agreement. Upon receiving the agreement, the peer designated port transitions to the forwarding state.¹⁰

:protocol rstp
S1 @1,0 prio=4096 icon= 
S2 @1,1
S3 @0,2
S4 @2,2
S5 @0,3 prio=8192 icon= 
S6 @2,3
H1 @0,1.2   proto=none icon= 
H2 @2,1.2   proto=none icon= 
H3 @2.5,1.3 proto=none icon= 
H4 @2.5,2.3 proto=none icon= 
S1 -- S2
S2 -- S3
S2 -- S4
S3 -- S5
S4 -- S6
S4 -- S3
S5 -- S6
S3 -- H1 S3:edge
S4 -- H2 S4:edge
S4 -- H3 S4:edge
S6 -- H4 S6:edge

In the topology above, H1, H2, H3, and H4 are end devices not participating in the protocol. We configure the ports they connect to as edge ports, so these ports immediately move to the forwarding state. Use the step button to move the simulation forward. The clock moves to 1 second. Step again and S1 and S2 send a proposal to each other. Here is the proposal from S2:

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    BPDU flags: 0x4e, Agreement, Port Role: Designated, Proposal
        0... .... = Topology Change Acknowledgment: No
        .1.. .... = Agreement: Yes
        ..0. .... = Forwarding: No
        ...0 .... = Learning: No
        .... 11.. = Port Role: Designated (3)
        .... ..1. = Proposal: Yes
        .... ...0 = Topology Change: No
    Root Identifier: 32768.02:00:00:00:00:01
    Root Path Cost: 0
    Bridge Identifier: 32768.02:00:00:00:00:01
    Port identifier: 0x8001

S1 ignores it: its own root identifier is lower. When S2 receives a similar proposal from S1, it accepts S1 as its root bridge. It also elects the port to S1 as the root port and starts the synchronization process. The two designated ports are already discarding, so no change here. Step again and S2 sends two BPDUs to S1. In one of them, the agreement bit is 1 and the proposal bit is 0. It also shows that S2 accepted S1 as the root bridge and its root port is now in the forwarding state. When receiving this BPDU, S1 transitions its own designated port to the forwarding state. From this point, the link between S1 and S2 forwards user traffic.

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    BPDU flags: 0x79, Agreement, Forwarding, Learning, Port Role: Root, Topology Change
        0... .... = Topology Change Acknowledgment: No
        .1.. .... = Agreement: Yes
        ..1. .... = Forwarding: Yes
        ...1 .... = Learning: Yes
        .... 10.. = Port Role: Root (2)
        .... ..0. = Proposal: No
        .... ...1 = Topology Change: Yes
    Root Identifier: 4096.02:00:00:00:00:00
    Root Path Cost: 20000
    Bridge Identifier: 32768.02:00:00:00:00:01
    Port identifier: 0x8001

Let s look at what happened to S5. Reset the simulation and step twice. S5 exchanges BPDUs with both S3 and S6. Since S5 has a lower root identifier than S3 and S6, it stays the root bridge, while S3 and S6 accept the proposal and elect their root ports. S3 and S6 start the synchronization process. S6 s port to H4 keeps forwarding because it is an edge port. Move one step. Both S3 and S6 send an agreement back to S5, which transitions both designated ports to the forwarding state. Yet, the link between S5 and S3 keeps discarding user traffic! If you look carefully, S3 s port toward S5 is now a designated port, not a root port. During the same step, S3 also receives a better BPDU from S2 with S1 as the root bridge. It elects its port to S2 as the root port and downgrades the port to S5 to a designated port, which stays in the discarding state. On the next step, things get a bit tricky. S3 sends a proposal to S5:¹¹

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    BPDU flags: 0x4f, Agreement, Port Role: Designated, Proposal, Topology Change
        0... .... = Topology Change Acknowledgment: No
        .1.. .... = Agreement: Yes
        ..0. .... = Forwarding: No
        ...0 .... = Learning: No
        .... 11.. = Port Role: Designated (3)
        .... ..1. = Proposal: Yes
        .... ...1 = Topology Change: Yes
    Root Identifier: 4096.02:00:00:00:00:00
    Root Path Cost: 40000
    Bridge Identifier: 32768.02:00:00:00:00:02
    Port identifier: 0x8002

S5 elects S1 as its root bridge and the port toward S3 as its root port. It starts its synchronization process, but the designated port to S6 does not move into the discarding state. Why? That port stays a designated port and its neighbor S6 has already sent an agreement on the link, so the port keeps its synced status. Now, let s step back to look at what happens to S6. At this point, S6 believes S5 is the root bridge. Step once and S4 sends a new proposal to S6. S6 accepts the proposal, elects S1 as the root bridge and the port to S4 as its root port. The role of the port facing S5 changes: from a root port, it becomes a designated port. Because its peer keeps advertising an inferior BPDU on the link, this port becomes disputed and moves to the discarding state. The root port transitions to the forwarding state and the link starts forwarding immediately because S4 s designated port is already in the forwarding state. If we step one more time, S5 and S6 exchange two BPDUs. The one from S5 is better because of its lower bridge identifier. S5 s port stays a designated port, while S6 downgrades its own port to an alternate port. Let s rewind to the start one last time: cut the link between S1 and S2, run the simulation until the topology is stable, stop the simulation, and restore the link between S1 and S2. During the first step, S1 and S2 exchange proposals. S2 elects S1 as the root bridge instead of S5 and the port to S1 as the root port. It downgrades the previous root port to a designated port and moves it into the discarding state. The other designated port stays synced and keeps its forwarding state. At the next step, S2 sends an agreement to S1 and the link between them starts forwarding user traffic. It also sends a proposal to S3, but not to S4. Instead, it sends a regular BPDU to S4. S4 still elects S1 as its root bridge and the port to S2 as its root port. It demotes its previous root port, the one to S3, to a designated port, which transitions to the discarding state because of the root port change. The other alternate port, to S6, also becomes a designated port and stays in the discarding state. The new root port moves to the forwarding state. On the next step, S4 s port to S3 settles as an alternate port after receiving a better BPDU from S3. RSTP is a giant state machine split into smaller ones: bridge detection, port information, port protocol migration, port role selection, port role transitions, port receive, port state transitions, port timers, port transmit, and topology change. Some of them are per bridge, some per port. Each bridge runs an instance. Time, operational port state changes, and the BPDUs it receives from other instances drive the transitions. Being event-driven makes RSTP more efficient but also more difficult to understand.

Placeholder for the Port Information state machine extracted from IEEE 802.1Q-2005, page 182. Pending IEEE authorization for reproduction, this is the blueprint for the Western Australian Government Railways class Msa Garratt articulated steam locomotive.

Topology change notification A bridge populates a MAC address table: it associates each source MAC address with the port that last received it. When forwarding an Ethernet frame, it looks up this table to choose the right port.¹² When a link fails, a connected fridge reachable through one port may become reachable through another one. The affected bridges should flush the MAC addresses they learned, because these entries may now be wrong. For this purpose, RSTP implements topology change notifications using a flooding mechanism. When a non-edge port transitions to the forwarding state, a bridge generates BPDUs with the topology change (TC) bit set. It sends them to all the non-edge designated ports and to the root port. It also flushes the MAC address table on these ports. When a bridge receives such a BPDU, it propagates the notification to all non-edge designated ports and the root port, except the one the notification came from. It also flushes the MAC address table on these ports. In the examples, the BPDUs with the TC bit set to 1 have a red circle.

:protocol rstp
S1 @1,0 prio=4096 icon= 
S2 @0,1
S3 @1,1
S4 @2,1
S5 @1,2
LPT @0.1,2 proto=none icon= 
S1 -- S2
S1 -- S3
S1 -- S4
S2 -- S3
S2 -- S5
S4 -- S5
S5 -- LPT S5:edge

Start the simulation and wait a few seconds for the topology to settle. Stop the simulation and disable the link between S2 and S5. S5 elects the port facing S4 as the root port, which transitions immediately to the forwarding state. Step once and S5 emits a BPDU with the TC bit set to 1:

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    BPDU flags: 0x79, Agreement, Forwarding, Learning, Port Role: Root, Topology Change
        0... .... = Topology Change Acknowledgment: No
        .1.. .... = Agreement: Yes
        ..1. .... = Forwarding: Yes
        ...1 .... = Learning: Yes
        .... 10.. = Port Role: Root (2)
        .... ..0. = Proposal: No
        .... ...1 = Topology Change: Yes
    Root Identifier: 4096.02:00:00:00:00:00
    Root Path Cost: 40000
    Bridge Identifier: 32768.02:00:00:00:00:04
    Port identifier: 0x8002

S4 receives this BPDU. It flushes the MAC address table on the port facing S1: while LPT was previously reachable through this port, it is now reachable through S5. Step once. S4 sends S1 a BPDU with the TC bit set to 1. When S1 receives this BPDU, it flushes the MAC address table on the ports facing S2 and S3. Step once and S1 sends a notification to S2 and S3. Step once again and S2 sends a notification to S3, while S3 does nothing because the port toward S2 is an alternate port. S3 does not flush any MAC address table: LPT is still reachable through its port to S1. If you step a bit more, you will see that some of the periodic BPDUs keep the TC bit set to 1. Each port runs a timer equal to the hello timer plus one second.¹³ The timer starts when the port emits a notification. Until it expires, the port sets the TC bit to 1 in every BPDU it sends. You can also see some periodic BPDUs without the TC bit: they originate from a port that only received a notification and therefore did not arm its timer.

Security RSTP is weak against configuration errors and malicious actors. A bridge not talking RSTP can create a loop. An attacker can insert themselves into the topology to disrupt the service, spy on the traffic, or alter it. To mitigate such problems, you need to identify the edge ports. An edge port connects to an end device, like a PC or a printer. Such devices do not generate BPDUs and cannot create a loop. RSTP defines two related flags:

• When true, AdminEdge initializes a port as an edge port. It defaults to false.
• When true, AutoEdge lets a port become an edge port when it does not receive BPDUs for 3 seconds. It defaults to true.

If an edge port receives a BPDU, regardless of the values of these two flags, it reverts to a non-edge port.

R0 @1.5,1.5 prio=8192
# AutoEdge=true, AdminEdge=false, bridge
S1 @3,1.58
R0 -- S1
# AutoEdge=true, AdminEdge=false, end device
H1 @2.84,2.18 icon=  proto=none
R0 -- H1
# AutoEdge=true, AdminEdge=true, bridge
S2 @2.18,2.84
R0 -- S2 R0:edge
# AutoEdge=true, AdminEdge=true, end device
H2 @1.58,3 icon=  proto=none
R0 -- H2 R0:edge
# AutoEdge=false, AdminEdge=true, bridge
S3 @0.68,2.76
R0 -- S3 R0:edge R0:no-auto-edge
# AutoEdge=false, AdminEdge=true, end device
H3 @0.24,2.32 icon=  proto=none
R0 -- H3 R0:edge R0:no-auto-edge
# AutoEdge=false, AdminEdge=false, bridge
S4 @0,1.42
R0 -- S4 R0:no-auto-edge
# AutoEdge=false, AdminEdge=false, end device
H4 @0.16,0.82 icon=  proto=none
R0 -- H4 R0:no-auto-edge
# Network port, bridge
S5 @0.82,0.16
R0 -- S5 R0:network S5:network
# Network port, end device
H5 @1.42,0 icon=  proto=none
R0 -- H5 R0:network
# AdminEdge=true, bpdu-guard=true, bridge
S6 @2.32,0.24
R0 -- S6 R0:bpdu-guard R0:edge
# AdminEdge=true, bpdu-guard=true, end device
H6 @2.76,0.68 icon=  proto=none
R0 -- H6 R0:bpdu-guard R0:edge

In the topology above, S1, S2, S3, S4, S5, and S6 act as bridges, while H1, H2, H3, H4, H5, and H6 act as end devices:

• S1 and H1 are on a port without a specific configuration: AutoEdge is true, AdminEdge is false,
• S2 and H2 are on a port where AdminEdge is true,
• S3 and H3 are on a port where AutoEdge is false and AdminEdge is true,
• S4 and H4 are on a port where AutoEdge is false.

If you start the simulation and wait about 20 seconds, links to S1, S2, S3, S4, H1, H2, H3, and H4 eventually forward user traffic: none of the flags matter. But what about the two remaining pairs? S5 and H5 connect to a network port. Such a port enables a non-standard feature: bridge assurance. The port transmits BPDUs regardless of its role. If it does not receive BPDUs for 3 consecutive hello periods, it transitions to the discarding state. On the link between R0 and S5, you can see BPDUs traveling in both directions, unlike the other links, where only designated ports send BPDUs. S6 and H6 connect to a port where AdminEdge is true and BPDU guard is enabled. This is another non-standard feature that shuts down a port if it receives a BPDU. In summary, if you expect a port to be an edge port, you should set AdminEdge to true and enable BPDU guard. Otherwise, declare it as a network port.

Why RSTP today? A compelling use case for RSTP today is an out-of-band network for a datacenter, since you can tolerate an outage of a few seconds. The configuration is minimal and you can use cheap switches, like a Cisco 2960X.¹⁴ You need two switches acting as root bridges, and you build several loops to connect OOB switches in each cabinet. This simple design survives one failure on each loop.¹⁵

:protocol rstp
:tx-hold 10
# Root bridges
R1 @0,1 prio=0
R2 @0,2 prio=4096
R1 -- R2 cost=200 R1:network R2:network
R1 -- R2 cost=200 R1:network R2:network
# First loop
C1  @1,0 icon= 
C4  @2,0 icon= 
C7  @3,0 icon= 
C10 @4,0 icon= 
C12 @5,0 icon= 
C13 @5,3 icon= 
C15 @4,3 icon= 
C18 @3,3 icon= 
C21 @2,3 icon= 
C24 @1,3 icon= 
R1  -- C1  R1:network C1:network
C1  -- C4  C1:network C4:network
C4  -- C7  C4:network C7:network
C7  -- C10 C7:network C10:network
C10 -- C12 C10:network C12:network
C12 -- C13 C12:network C13:network
C13 -- C15 C13:network C15:network
C15 -- C18 C15:network C18:network
C18 -- C21 C18:network C21:network
C21 -- C24 C21:network C24:network
C24 -- R2  C24:network R2:network
# Second loop
C2  @1,0.5 icon= 
C5  @2,0.5 icon= 
C8  @3,0.5 icon= 
C11 @4,0.5 icon= 
C14 @4,2.5 icon= 
C17 @3,2.5 icon= 
C20 @2,2.5 icon= 
C23 @1,2.5 icon= 
R1  -- C2  R1:network C2:network
C2  -- C5  C2:network C5:network
C5  -- C8  C5:network C8:network
C8  -- C11 C8:network C11:network
C11 -- C14 C11:network C14:network
C14 -- C17 C14:network C17:network
C17 -- C20 C17:network C20:network
C20 -- C23 C20:network C23:network
C23 -- R2  C23:network R2:network
# Third loop
C3  @1,1 icon= 
C6  @2,1 icon= 
C9  @3,1 icon= 
C16 @3,2 icon= 
C19 @2,2 icon= 
C22 @1,2 icon= 
R1  -- C3  R1:network C3:network
C3  -- C6  C3:network C6:network
C6  -- C9  C6:network C9:network
C9  -- C16 C9:network C16:network
C16 -- C19 C16:network C19:network
C19 -- C22 C19:network C22:network
C22 -- R2  C22:network R2:network

This topology converges in about 6 seconds. Each loop should stay small (around 16 bridges) to reduce the probability of a double failure and to avoid sharing too much bandwidth. The design can evolve a bit without adding too much complexity: one VLAN per loop or one bridge domain per loop.

How large can a network be? The maximum age, whose default value is 20, governs the maximum distance of a bridge from the root. The topology below is too big for BPDUs from R1 to reach beyond S20.¹⁶

:protocol rstp
:tx-hold 10
:max-age 20
R1 @0,0 prio=4096 icon= 
R2 @0,5 prio=4096 icon= 
S1  @1,0
S2  @2,0
S3  @3,0
S4  @4,0
S5  @5,0
S6  @6,0
S7  @6,1
S8  @5,1
S9  @4,1
S10 @3,1
S11 @2,1
S12 @1,1
S13 @1,2
S14 @2,2
S15 @3,2
S16 @4,2
S17 @5,2
S18 @6,2
S19 @6,3
S20 @5,3
S21 @4,3
S22 @3,3
S23 @2,3
S24 @1,3
S25 @1,4
S26 @2,4
S27 @3,4
S28 @4,4
S29 @5,4
S30 @6,4
S31 @6,5
S32 @5,5
S33 @4,5
S34 @3,5
S35 @2,5
S36 @1,5
R1  -- S1
S1  -- S2
S2  -- S3
S3  -- S4
S4  -- S5
S5  -- S6
S6  -- S7
S7  -- S8
S8  -- S9
S9  -- S10
S10 -- S11
S11 -- S12
S12 -- S13
S13 -- S14
S14 -- S15
S15 -- S16
S16 -- S17
S17 -- S18
S18 -- S19
S19 -- S20
S20 -- S21
S21 -- S22
S22 -- S23
S23 -- S24
S24 -- S25
S25 -- S26
S26 -- S27
S27 -- S28
S28 -- S29
S29 -- S30
S30 -- S31
S31 -- S32
S32 -- S33
S33 -- S34
S34 -- S35
S35 -- S36
S36 -- R2
R1  -- R2 cost=200 down

Once the topology settles, part of the network considers R1 the root, while the other votes for R2. At the boundary, S20 tries to start a synchronization with S21 to move its designated port to the forwarding state. The BPDU looks like this:

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    BPDU flags: 0x4e, Agreement, Port Role: Designated, Proposal
    Root Identifier: 4096.02:00:00:00:00:00
    Root Path Cost: 400000
    Bridge Identifier: 32768.02:00:00:00:00:15
    Port identifier: 0x8002
    Message Age: 20
    Max Age: 20

S21 rejects it because the message age equals the maximum age. On the other hand, the BPDU S21 sends to S20 looks like this:

Spanning Tree Protocol
    Protocol Identifier: Spanning Tree Protocol (0x0000)
    Protocol Version Identifier: Rapid Spanning Tree (2)
    BPDU Type: Rapid/Multiple Spanning Tree (0x02)
    BPDU flags: 0x7c, Agreement, Forwarding, Learning, Port Role: Designated
    Root Identifier: 4096.02:00:00:00:00:01
    Root Path Cost: 320000
    Bridge Identifier: 32768.02:00:00:00:00:16
    Port identifier: 0x8001
    Message Age: 16
    Max Age: 20

This is not enough to change S20 s root port because S20 has a lower root identifier 4096.02:00:00:00:00:00 vs 4096.02:00:00:00:00:01. Fixing the link between R1 and R2 resolves the issue. The maximum message age any packet carries is now 18, below the configured maximum age. But it only works until another link breaks. A plausible fix is to increase the maximum age to 40.¹⁷

How fast is RSTP? RSTP usually converges in a couple of seconds at startup. It often repairs a tree in less than a second. Even the 38-bridge topology takes less than 10 seconds to converge.¹⁸ Some topologies can take a bit more time to recover when the root bridge becomes unavailable.¹⁹

:protocol rstp
R0 @1,0 prio=0
S1 @1,1 prio=4096
S2 @0,2 prio=8192
S3 @2,2
R0 -- S1
S1 -- S2
S2 -- S3
S3 -- S1

In the topology above, start the simulation, wait for convergence, hit stop, and cut the link between R0 and S1. The topology is already optimal, but RSTP has a hard time converging again. First, S1 loses its root port. It has no more information about R0 and elects itself as the root bridge. It keeps its ports to S2 and S3 as designated ports in the forwarding state. Step once and it sends a BPDU to both S2 and S3 to let them know about the root change. When receiving it, S2 accepts S1 as its root because it does not have a better root on another port. It elects the port to S1 as its root port. The other port stays a designated port. Both ports keep forwarding. When receiving the BPDU from S1, S3 behaves differently: it knows R0 as a better root than S1 through its alternate port to S2. It promotes this port to a root port and demotes the port facing S1 to a designated port, which requires a new agreement. Step once and S3 sends a proposal to S1 with R0 as the root bridge. S1 elects R0 as the root bridge and promotes its port to S3 as a root port. During the same step, S3 also receives a BPDU from S2 stating that S1 is the root bridge. Therefore, S3 has no port left with R0 as the root bridge: it elects S1 as the root bridge and its port to S2 as the root port. Step once and its next BPDU to S1 includes this information: S1 elects itself again as the root bridge. But during the same wave, S1 sends a proposal to S2 with R0 as the root bridge. While S1 and S3 agree that S1 is the root bridge, S2 now believes this is R0! In turn, S2 again convinces S3 that R0 is the root bridge, S3 convinces S1, S1 convinces S2, and S2 convinces S3. This could go on forever, but it does not. The BPDUs saying R0 is root eventually age out when the message age goes past the maximum age. In the example above, at the eleventh second, S2 sends a BPDU to S3 with R0 as root, but S3 drops it because its message age reached the maximum. With some luck, the topology can also converge faster if a port stops transmitting new BPDUs after tripping the transmit hold count, whose default value is 6 per second.

About MSTP MSTP is the VLAN-aware version of RSTP: it runs several instances of RSTP and lets the administrator map each VLAN to a specific instance. For example, you can map VLANs 100 to 200 to a first instance, and 300 to 400 to a second instance. The remaining VLANs map to a special instance named the Internal Spanning Tree (IST). MSTP adds its own complexity, but the gist is that you have several logical topologies acting independently. If you want to dig deeper, have a look at MSTP Tutorial Part I: Inside a Region.

About the interactive examples The interactive examples run MSTPD directly in your browser, compiled to WebAssembly with emscripten. A C API replaces the code talking to the Linux kernel: it manages bridges and ports, exports state as JSON, and drives time deterministically. A JavaScript wrapper makes it more user-friendly:

import   loadMSTPD   from "./dist/mstpd.mjs";
const mstp = await loadMSTPD();
// Create 3 bridges
const a = mstp.createBridge("A",   priority: 4096  );
const b = mstp.createBridge("B",   priority: 8192  );
const c = mstp.createBridge("C");
// Each bridge has two ports
const a1 = a.addPort("a-b",   portno: 1  );
const a2 = a.addPort("a-c",   portno: 2  );
const b1 = b.addPort("b-a",   portno: 1  );
const b2 = b.addPort("b-c",   portno: 2  );
const c1 = c.addPort("c-a",   portno: 1  );
const c2 = c.addPort("c-b",   portno: 2  );
// Build a triangle topology
mstp.link(a1, b1);
mstp.link(a2, c1);
mstp.link(b2, c2);
// Enable all bridges and ports
for (const br of [a, b, c]) br.enable();
for (const p of [a1, a2, b1, b2, c1, c2]) p.enable();
// Execute 40 seconds' worth of wall clock and display the topology
mstp.step(40);
console.log("Topology:", mstp.topology());

Several dozen unit tests explore the features of MSTPD and check that they work correctly in this environment:

$ node --test *.test.mjs
  two bridges: lower priority becomes root (41.657342ms)
  triangle loop: exactly one port blocks and all agree on the root (5.832ms)
  breaking the active link reconverges and restoring recovers (18.730753ms)
[ ]
  tests 40
  pass 40
  fail 0
[ ]
  duration_ms 396.190897

Additional JavaScript code looks for specific <pre> blocks containing a topology definition and turns them into interactive widgets. You can inspect and modify the definition by hitting the edit button. There is also a cool trick to tell whether the topology has converged. After each step, we save a snapshot of the simulation memory, play 50 seconds worth of simulation to check if the topology is stable, and travel back in time by restoring that snapshot. The complete code lives on GitHub. I am happy with the result. It can be difficult to follow everything happening during a single step, but stepping forward and backward helps. I plan to use the same approach in future blog posts about networking features.

Note Michael Lynch reviewed a first draft of this article. He authored Refactoring English, a book to sharpen your writing for blog posts, documentation, commit messages, and tutorials. Any errors are still mine!

---

1. The sprites for Stan and Blobby come from Craftpix, the coffee cups from Yanin.
2. STP was introduced in IEEE 802.1D-1990. It is still present in IEEE 802.1D-1998 but was withdrawn in IEEE 802.1D-2004 in favor of RSTP, introduced in IEEE 802.1w-2001. MSTP was introduced in IEEE 802.1s-2002 and merged into IEEE 802.1Q-2003. Both of them are part of IEEE 802.1Q-2022 along with SPB a protocol I had never heard of until writing this article.
3. From here, I use bridge instead of the more common word switch.
4. The Linux kernel only runs STP. It delegates the other protocols to user space.
5. MSTPD implements the state machine from IEEE 802.1Q-2005, but on Linux it runs RSTP only. Linux 5.18 added support for forwarding multiple spanning tree, but MSTPD does not use it yet. See PR #150 for progress on this front.
6. The priority is a multiple of 4,096: with MSTP, the lower 12 bits of the bridge priority encode the MST instance identifier, leaving only the upper 4 bits for the configured priority.
7. To inspect the BPDUs crossing a link, select it, click the Download packets button, and open the file with Wireshark.
8. A backup port only exists if the bridge has several ports on the same collision domain. This should not happen in a switched network.
9. This is the value of the hello timer. It used to be configurable, but IEEE 802.1Q-2005 pins it to 2. MSTPD does not allow another value.
10. If the peer port does not receive an agreement after the hello timer elapses or the maximum age if the port has just come up it falls back to the timer-based method for compatibility with STP: it transitions to the learning state, waits again for the hello timer to expire, and transitions to the forwarding state.
11. As in many proposals, S3 also sets the agreement bit to 1. The proposal bit says I am the designated port on this link and I want to transition to the forwarding state. The agreement bit says I am already in sync with the rest of my bridge on this root information. Both can be true.
12. If it finds no entry, the bridge duplicates the Ethernet frame on all ports, except the incoming one. The same happens if the destination MAC address is the broadcast one (ff:ff:ff:ff:ff:ff). This behavior bootstraps the learning process.
13. This timer makes RSTP resistant to packet loss.
14. You can get them for less than US$100 through a broker. All the ports run PVST+ by default and automatically fall back to plain RSTP.
15. An alternative would be Ethernet Ring Protection Switching (ERPS) another protocol I had never heard of until researching this article.
16. If you look closely at what happens at t=2s, you can see that R2 is gaining popularity as root: S17 to S36 believe R2 is the root bridge. S16 does not follow because we hit the maximum age. Later, S17 to S20 reverse their position. I ll let you explore the state of the various bridges to understand the root cause.
17. When increasing the maximum age to 40, you also need to increase the forward delay to 21 (:forward-delay 21), as the standard enforces this condition: 2 (Forward Delay 1) Max Age. For this specific topology, you could also increase the maximum age to 37 and forward-delay to 20.
18. The simulation may seem slow, but it does not run in real time. Look at the current timestamp in the upper right corner to know the wall clock, e.g. t=8s. Once the topology stabilizes, the same corner shows the convergence time, e.g. 2s.
19. Khaled Elmeleegy, Alan Cox, and Eugene Ng formalized this phenomenon in On Count-to-Infinity Induced Forwarding Loops in Ethernet Networks and later in Understanding and Mitigating the Effects of Count to Infinity in Ethernet Networks. They propose a fix that did not find its way into a standard.

Programming language generations When I was young, I learned about a model of classifying programming language: the system of programming language generations. In this model, first generation programming languages are, basically, where you program the computer in the language that is defined by its architecture. On a Von Neumann machine, with its load-and-store architecture, you do that by inputting a string of numbers. The first programmer in human history -- her name was Ada Lovelace -- wrote in a first-generation language. 1GLs aren't so much invented as they are a byproduct of the computers for which they're created. Second-generation languages are the assembler languages. Because humans are not computers, and because decoding long lines of numbers to understand what the computer is doing, when programming became a full-time job, the programmers that did it decided that doing all this assembling manually is too complicated, so they quickly wrote assemblers to automate the process for them. They still could understand the 1GL output of the 2GL assembler, but most of them quickly forgot how to write software in a first-generation language. Not that anyone cared, as the translation from a 2GL to a 1GL is lossless and you can just revert it. Third-generation languages are higher-level languages. When the first 3GLs were invented (such as COBOL and, more famously, FORTRAN) in the late 1950s and early 1960s, it was believed by some that the work of programming a computer so accessible to non-programmers that the job of programmer would eventually cease to exist, and people would just ask the computer what they needed by entering COBOL instructions. This of course was ridiculous and incorrect, because converting algorithms to computer instructions, whether at the 2GL or 3GL level, is a specialized skill that some automation can perhaps make simpler but never completely take away the need for. At the time, some people also felt to some extent that using 3GL wasn't the same thing as actually programming 3GLs, but eventually the world moved on and embraced things. The invention of 3GL environments reduced, but did not completely take away, the need for people to understand 2GLs, as compiler and operating system authors still need to understand them, and some highly optimized code still continues to be written in 2GLs to this day. Fourth-generation languages abstract away some or all of the process of programming. For instance, a database-related 4GL will hide away the complexities of storing data in particular locations, how to fetch that data, how to index it such that you can fetch it efficiently, how to loop over the data to get you a summary of that data, and instead allows you to express the required information in an abstract way, expecing the computer to fill in the blanks. When SQL, an early 4GL, was invented, some people believed that the language made accessing databases so simple that the requirement to implement database applications would eventually cease to exist and we would just hand SQL prompts to users who need to access data. This of course was ridiculous and incorrect, because understanding data schemas and using that understanding to query data from a database is a specialized skill that perhaps a higher abstraction can help you make simpler, but that in the longer run it can never completely take away the need for. The invention of 4GLs also reduced, but did not completely take away, the need for people to understand how to do the things that the 4GLs automate for you manually, as the people who do write those things still need to understand them, and there are also environments where these particular 4GLs are rather not appropriate or just very slow. The first definition of programming language generations that I read about in the 1980s simply stated that fifth-generation languages did not yet exist, but that they would in the future, and that in those, you would "tell the computer what to do, and it would then do that". Now that we have a way of doing so, it could be said that by some definition, we now actually do have a number of 5GLs. The existence of these LLM systems has caused some, especially the people who build and exploit these systems, to exclaim that programming as we know it today is going to cease to exist, and everyone will just ask an LLM to generate a program, which will then do so. That is of course ridiculous and incorrect, as no automaton can generate software from nothing; input is still required for the model to be able to produce something that approaches usability, and being able to word that input in a correct and productive fashion will be a skill that future programmers can benefit from. I ran some experiments a while back, and from that concluded that, if we look only at the technical side, LLM use can, in some niches, increase productivity for a programmer. There are certainly things that you shouldn't use an LLM for, but equally there can be cases where use of an LLM to perform some task that traditionally would have been done by a programmer would be a net positive. But LLMs, as they exist today, are highly problematic. They require vast amounts of data to build the model. The companies that build these models are disrespectful of people who run web services, and as a result, everyone now has to implement various types of application firewalls just to not make systems fall over from the overwhelming requests for data. They are also disregarding the licenses that are attached to these vast amounts of data, which makes me, as a person who believes in the tenets of free software, sad. They require vast amounts of energy, causing an already-critical global warming crisis to, well, not improve. They require vast amounts of coolant to dissipate the energy concentrated in their data centers, causing further environmental effects. In this, they are problematic and to be avoided. But these are side states of the current state of affairs; I do not believe that they are inherently implied to be able to build and operate an LLM -- any LLM. I guess it's fair to say that my feelings towards LLM usage are complex and many-faceted. I haven't been involved in many debates about the subject, debates that to me seem to be mostly focused on "LLM good" vs "LLM bad" arguments that aren't as nuanced as the position that I would believe is more accurate. This is not because I don't care, but partially because I've been busy in my personal life recently and partially because the whole thing seems somewhat disheartening. But then Debian popped up GR 2026-002, meaning, I now have to come up with an opinion about various candidate statements in the context of the above, which is... not easy. But I did it anyway. There are 8 choices on the ballot, and they all have some truth and some falsehood to them. My position about LLMs can be summarized as:

• The current state of affairs wrt LLMs is disastrous and we should not encourage them
• However, there's no technical reason why this must remain true for all time
• And so any statement should keep in mind what might happen in the future and that the current disastrousness of the whole thing isn't guaranteed to continue to exist for all eternity.

With that, let's go over them.

GR vote options

Proposal A Its summary, from the GR text:

This proposal aims to expressly forbid any contributions to Debian written with the use or assistance of large language models (LLMs) or other generative AI tools.

This falls squarely in the "LLM bad" camp, outlawing all generative-AI contributions, disregarding potential future ones where the problematic situations that exist today are not present. It makes a change to the social contract, which is especially difficult to reverse (on purpose), and which therefore also will require a 3:1 supermajority, but if we want to ban LLM-assisted contributions, this is probably the best way to do it.

Proposal B This one tries to allow AI-assisted contributions under certain conditions. It's mostly an "LLM good" proposal, with some caveats that can be discribed as "make sure you know what you're doing".

Proposal C This proposal is both a weaker (in some places) and stronger (in other places) version of Proposal A. It makes changes to the code of conduct instead of to the social contract, and it also wants to, at least, suggest policy to parties beyond the Debian project. By not changing the social contract, however, it is more likely to reach its simple majority requirement than proposal A. I don't think the language that it wants to add to the code of conduct is particularly well phrased, however.

Proposal D This is a weaker form of proposal B. The language is more compact and there are a few requirements that are spelled out in proposal B that are not spelled out in proposal D, but if you read between the lines you'll see that the requirement is still there really and I don't understand why proposals B and D were not merged into one.

Proposal E This proposal tries to hold a middle ground between "LLM good" and "LLM bad". It appreciates that things are quite muddled at the present time, and that perhaps the situation might might change in the future. It acknowledges that certain questions remain unanswered and that perhaps future considerations might therefore be different. But it essentially refuses to take a stance on whether LLMs should be accepted by the project or not.

Proposal F Similar to proposal E, this proposal tries to discourage Debian contributors from using LLMs, while still allowing people to use it should they want to, but with some requests and requirements to mark LLM-assisted contributions to account for those people who don't want to interact with LLM-generated software. As such, it is a proposal similar to proposal E that leans closer to the "LLM bad" camp.

Proposal G

This proposal aims to ensure that contributions directly to Debian are created by humans, while at the same time avoiding restrictions on the tools those humans may choose to use when contributing

Another "LLM bad" proposal, it however restricts the "bad" bits to only the direct output of the LLM. If you use an LLM to do something and then clean-room re-implement the same thing yourself, that's apparently fine.

Proposal H This proposal condemns the use of LLM for its environmental and moral problems, but explicitly not for its technical considerations. I feel that it is closest to my position as explained above.

Voting Expressing a vote on a ballot so convoluted and complicated like this one takes time. I have to read and understand every ballot option, and formulate an order of them. And I shouldn't just state which option has my preference; Debian's voting process allows a rich expression of opinion on ballot options. Anyway, I eventually ended up voting in a way that I think is consistent with my opinion. But it wasn't easy.

• Proposal A(1) - Action: ban all AI-assisted contributions via Social Contract amendment, except from upstreams (so not rolling back the Linux kernel and other software to "pure", pre-AI state). Claims that copyright/licensing status is unclear, quality is bad, community is being destroyed, web resources see extra load and that training consumes "staggering" resources. Needs 2/3rd majority to pass. - IMHO worst and most inconsistent. If copyright and licensing of AI products is unclear, then be consistent - ban ALL software with AI contributions, fork Linux kernel and other software from pre-AI versions, reject all security fixes of issues found with AI. Quality section lists problems that have not existed in the real world since at least a year of rapid AI coding development. Community section assumes that now all Debian contributions will be drive-by AI slop and no one will learn anything anymore. Ethics section mixes up effects of badly configured systems (AI web load is no different from load from a badly configured Perl script) with claimed "resource" usage without any context, taking on trust project ambitions of startups and assuming exponential growth. And then concludes that delivering less is in the interest of our users somehow.
• Proposal B(2) - Action: allow AI-assisted contributions, with conditions of: legality, accountability, disclosure, no uncoordinated bulk actions, privacy. Concerns on quality and legal status as well as environmental impact and scraper load are noted, but not really addressed beyond labelling them as concerns. - IMHO it is an ok starting position as it establishes that each contributing person must still be fully responsible for their contribution (both legally and technically) and for that has to also understand (and review) what they submit. Disclosure lets others know to watch out for other classes of problems when code was changed with AI assistance. Prior discussion for bulk changes just says that the (already established) practice should not be neglected just because now large changes are easier to do. And the privacy part warns against accidentally sending private or confidential data (like a not yet published security bug) to a public service where it could become public. Personally I would have liked a stronger statement to encourage use of environmentally responsible AI services and local AI tools. Possibly a preference for open-weight models with a clear path forward to preferring truly free AI models, when such a category of products could be clearly delineated and established.
• Proposal C(3) - Action: reject AI-assisted contributions at Code of Conduct level. Claims all the world's evils come from LLMs and that "Ethical and safe use of this technology is almost impossible". Goes as far as banning any use of LLMs even in Debian mailing list emails and Debian Planet blog posts - if you do, it's a CoC violation and may result in exclusion from the project. Additionally mandates the disclosure of the usage ... presumably to ban you more efficiently for it. - IMHO truly a dictatorial nightmare option. Zero actual reasoning or basis for such a decision. Zero sources. Nothing claimed in this option's rationale is even close to reality and nothing claimed there is in any way related to the actual technology being discussed. Like, an "LLM" does not automagically commit "fraud" when you use it, like this proposal claims, as if that was a well-known fact. LLMs are not all "owned by horrible people and companies". Even if some include a (prominent Debian user, long-time supporter and sponsor) Google into "horrible companies" (which is what this proposal implies!), there are plenty of LLMs owned by all kinds of companies all over the world and there are plenty of open-weight LLMs that are not really owned by anyone. Most invasive and dishonest option on the ballot.
• Proposal D(4) - Action: allow AI-assisted contributions, with conditions of: legality, accountability, disclosure, privacy. IMHO same as B, just shorter. Adds a "we don't recommend" towards others developing software with AI assistance. Seems pretty weird to add that and then immediately accept Debian contributors doing so. Assumes that the bulk change bit of B is implied as AI is just tooling, so bulk changes should be pre-discussed just like today - so no change and thus no point in mentioning that. Fair. D is a bit more explicit on expected technical details - like that the "person" submitting the change is supposed to sign it, not AI. Notable is the complete absence of resource usage or the environment from concerns. IMHO it would be better to have that and also recommendations on how to avoid causing environmental damage when using AI.
• Proposal E(5) - Action: no action as such - AI-assisted contributions must follow the same rules as all other contributions and those rules are sufficient. IMHO despite its length this is a very well-worded position statement that describes how and why AI-assisted contributions already work perfectly fine in the Debian context when all the same rules that apply to all contributions are also consistently applied to AI-assisted contributions. It describes how the same legality, accountability, no bulk change and privacy requirements are already in place and still apply and how AI-assisted contributions can and must still satisfy them. I could add again that some guidance would be nice here for both legal and environmental decisions when using AI, but in this case it does not really belong in this proposal itself. We as Debian do not have a document that requires that our non-AI-assisted contributions be made with only sustainably sourced electricity, for example. So why should AI be special one way or another? IMHO Debian should have a datacenter sustainability policy, regardless of the AI discussion.
• Proposal F(6) - Action: discourage AI, but allow it based on existing processes (similar idea to E). Dances a bit around the question of disclosure of AI use (as a courtesy) and accepting that some people may still ban all contributions where any AI was involved in any way. Which in turn discourages disclosure to avoid pointless rejection of valuable contributions (like security patches). IMHO this option is ok, but so watered down that it is bound to bring up further discussions and conflicts on details.
• Proposal G(7) - Action: ban non-humans from directly contributing to Debian. IMHO - another bizarre and self-contradictory option. It bans all Debian interactions with AI assistance, including email messages to Debian mailing lists and (supposedly) blog posts on Planet Debian. It "reminds" people who "use such tools assistively" of the DFSG and Social Contract - isn't that a threat of a ban and expulsion similar to C? The proposal does take pains to delineate where a contribution comes from AI as output (bad) vs when you are assisted by AI in the process of exploring, researching or maybe even reviewing the code, but you actually type all the code yourself and use the AI just as a taskmaster with a whip (good). And just like A or C it completely ignores how this inherently evil and unstable AI-generated code becomes perfectly fine and good as soon as someone develops that outside of the Debian project. Even if the same person then packages it for Debian the next day. It is hypocritical, unsustainable and ignores the needs of our users. Just like C it also bans someone writing an email or bug report in their native language and using a modern translation tool or service (that uses LLMs nowadays for better grammatical clarity) to translate that to English before sending it to a Debian mailing list or BTS. Heavy-handed and invasive. And the only reasoning provided for this is some unnamed "concerns" of "extra work" being borne by "other people"? Kind of does not feel right to bear such draconian restrictions for some unspecified concerns.
• Proposal H(8) - Action: condemn usage, but not actually ban anything. And then it goes on to claim (without any evidence or elaboration) that LLM usage accelerates the destruction of "planet earth" (sic). IMHO this proposal is at the same time the loudest ("The planet is burning") and also the one that demands the least action. It dances a really twisty line between raising "significant" concerns in all areas and even claiming that use of LLMs destroys the planet, flies by explicit condemnation of LLM usage and then suddenly collapses with not condemning LLM users and swinging to lamentations that it is actually impossible to impose policies on LLM usage or even detect when an LLM was used (which kind of directly contradicts bad quality claims from A, C and G) and lands on "encouraging" contributors not to use LLMs (where practical) and otherwise do nothing else. It's like this is a 5th draft that started off with the rationale and total ban like in C, but then got defanged so far that its action side no longer matches the rationale stated.

• Proposal E(5) - solid hack of integrating AI into already existing Debian rules and conventions
• Proposal B(2) - explicit and detailed
• Proposal D(4) - lower because of discouragement to others on what we agreed to do ourselves
• Proposal F(6) - I am not a fan of dancing around with disclosures
• Further discussion(9) - I do not want any option below this to succeed as they would do more harm than good
• Proposal H(8) - loud, but not doing anything actually
• Proposal A(1) - at least this one does not set rules for emails
• Proposal G(7) - at least this one allows an AI overseer to tell you what to write with your own fingers
• Proposal C(3) - the most draconic and invasive one that explicitly wants to kick people out of the project

• information on which AI services have Terms and Conditions that make them problematic for free software development, legally speaking
• information on which AI services do (or do not) achieve a sufficient level of sustainability to be worth recommending (and then do the same for other data centers we already use)
• information on which local AI models were trained in sustainable ways
• base-level prompts to set technical expectations on various types of contributions, like bug reports or patches to packaging or translations
• default configuration for AI-assisted code reviews on Salsa that projects could enable and supplement with their own instructions on top

• encourage them to improve sustainability (where needed)
• investigate and fix problems causing excessive scraping load on systems
• provide AI resources for Debian usage, for example in CI infrastructure or to provide equal development support opportunities for Debian developers who cannot afford paid AI services
• improve coding outputs of their models in the Debian context if/when systematic deficiencies in the output are found by us

Also in this series:

• The Four Horsemen of the LLM Apocalypse
• A more nuanced view of LLMs

How people use models People using LLMs in their workflow have radically changed how programming works, even for people who claim to avoid vibe-coding. And I'm sorry to single out one poor maintainer here: it's not you, Brian, you're just one example among many. But this is typical use of those models nowadays:

Once it s done, I ll use /code-review and let Claude spawn sub-agents to do a full review of the new code. This usually finds some problems, even problems that the main Claude instance didn t find during its validation. I usually keep running /code-review again and again after finding and fixing issues, until there aren t any left.

Think about what that means for a minute. This is automation built to fire up dozens of agents crunching at a problem for minutes if not hours of GPU compute time, in parallel. This is essentially a couple of shelves in a datacenter rack, totally maxed out on power and cooling, abstracted behind a cute little /code-review command. The author, here, is rightly concerned that "Anthropic could pull the rug out and require API pricing", which is perhaps a code word for "charging something closer to actual costs". Brian also pays lip service to environmental and societal costs but those are largely abstracted away, so let's keep that conversation aside here as well, as we have discussed it before anyways. But clearly, this way of working has an (externalized) cost, to say the least.

Paying for non-free tools For decades my work has been focused on free and open source software. I've long stopped using proprietary operating systems like Windows or Mac, and even before that switch, I was mostly using free software on those platforms, partly out of principle, but also because I was too poor. So the tools of my trade are free, and I build free tools with them. It feels like we're going backwards: when I was in school, a millennia ago, my classmates didn't have access to a compiler and were wondering how they would scrape the money to buy a compiler like Borland's or Microsoft's. I had a compiler built into my operating system (FreeBSD at the time), so that wasn't a problem for me. For them, it was a significant expense, but at least those expenses (or more shady sourcing of programs) were a one-shot deal. Fast forward 30 years, and software is rented: you pay monthly for Adobe's Photoshop and Microsoft's office suite just like you pay for Netflix, Disney+ or Spotify². And now you need to add dozens (if not hundreds of dollars) of monthly credits to access LLMs on top of that. So, now we have to pay to get anything done? This is peak enshitification of our job: first they steal our work to train their models, and then they sell it back to us at a profit.

Attacking the engineers AI is coming for our jobs, as engineers, if not everyone, according to the narrative. For a while now, our job market has deteriorated: less jobs, for less pay. Lots of skilled engineers looking for work and finding crap jobs then still looking while working. This is not by accident.³ We engineers have a lot of power, it is not organized, but that's just a couple of unions away (easy!). Tech overlords know this, so they are attacking our profession, directly, by forcing us to train and use models that they can control. Even in environments where programmers are not forced to use LLMs, the mere pressure of other people's LLM-generated work is huge. One can be forced to review LLM outputs, or just peer pressured you into producing more. We're now supposed to accelerate delivery, because models can presumably do things so much better and faster. With supply chain security becoming such a large vector that we now have worms crawling around developers accounts on NPM, increasing the delivery cadence seems like a really bad idea.⁴ The LLM hype is part of the larger wave of cyberwar against workers, against water, against the Earth, against all the people. This is not a matter of individually "adapting to the reality" or personal choice, but a political, social, hard problem we need to address collectively.

---

1. I again prefer the term LLM to "AI" because models do not possess intelligence. I did use it in the title because click baiting is apparently important, but I stopped short of calling this one "Rage Against the Machines" because that would be the title of every blog post I have ever made.
2. Yes, I know that Visual Studio is kind of free now, but I wouldn't be surprised if they turn that into a rental as well, because why not.
3. Beyond sabotaging the job market, Sam Altman event wants to sell "intelligence as a utility" something that is just a really bad idea but especially shows how megalomaniac those people are.
4. This brings back memories of another era, walking us back decades in terms of computer security.

For human developers, using coding LLMs magnifies their difference in skill levels.

• Packaging upstream software for Debian (by far the largest task)
• Writing Debian-specific code (eg, apt or the Debian installer)
• Maintaining Debian infrastructure (build systems, for instance)
• Writing documentation and translations

• Proposal A: seeks to amend the social contract, which I am opposed to for reasons already laid out above. It names some real concerns about AI that I agree with, but implies that all LLM uses and models are guilty of the problems, which is not the case with all of the claims. It also sets us behind the curve on security and stability by forbidding the use or assistance of those tools, even if run by others. It requires us to ignore reports of actual security bugs, or correct fixes, if those reports were generated with the assistance of an LLM, which I find to be absolutely untenable.
• Proposal B: This is the AI with accountability approach. It notes the real concerns with LLMs without painting with an overbroad brush. It strikes me as level-headed and sensible.
• Proposal C: It paints with an over-broad brush and makes some non-binding requests. Then it winds up largely like proposal B, though while it is worded more strongly, has fewer binding requirements (for instance, it lacks proposal B s prohibition on transmitting sensitive information to untrusted providers)
• Proposal D: Seems broadly similar to proposal B, an AI with accountability approach. I m not really clear why we need both.
• Proposal E: Largely the status quo. It is like proposals B and D in that it says humans are accountable for their contributions. It encourages disclosure of LLM use, but does not mandate it. Like proposal B, it prohibits disclosing sensitive information to third-party AI services. Note that both proposals B and D have an appropriate nuance: a local model is fine, a third-party one is not.
• Proposal F: This seems really similar to proposal E. I m not sure why we have these two.
• Proposal G: Disallows the output of generative AI as direct contributions to Debian. This is something of a weakened proposal A; it doesn t seek to amend the social contract, nor does it ban all use; it simply bans the use as a direct contribution.
• Proposal H: Ban due to climate impacts. How is this even an argument is disrespectful to reasoned conversation. I have already noted that LLMs can be and are used in ways that are not climate-harming. It explicitly contains no binding requirements at all, and is effectively a rant. While I agree with the sentiment that climate change is an urgent problem, and that some LLMs are exacerbating it, I disagree with that all LLM usage does so and therefore disagree with the conclusion.

2026-07-24 15:43:17 status triggers-pending wordpress:all 7.0+dfsg1-1
2026-07-24 15:43:19 upgrade wordpress:all 7.0+dfsg1-1 7.0.2+dfsg1-1

1. WordPress doesn t seem to store the version it s running at the time of operations. So anyone who doesn t have a suitable external log of versions deployed (such as the dpkg.log file for a Debian managed installation) won t know for sure which version was running. It supports automatic updates but you can t be sure that they happened soon after the release.
2. There is no log of IP addresses used for operations. There are apparently some 3rd party modules to log such things and web pages documenting how to modify the PHP to add it but nothing in the standard distribution.
3. Software should have a standard distribution with some support for logging of security relevant data. The typical situation is that people don t plan for logging such things until after they have been attacked so the data should be recorded without users going out of their way to log it.
4. A log of security relevant data should be stored in a database table with only insert access (no update, delete, or drop).
5. Ideally a CMS would support different database accounts for different purposes. Someone from an internal network or VPN could talk to an instance of the web server which has a database username and password giving full access. Everyone from outside the trusted range gets an instance of the web server with database access only allowing to read the posts and appearance configuration and to enter comments. If the database didn t allow the account used for public access to create new admin users or create posts then it would be a lot harder for attackers.
6. Ideally for everything that stores user account data there would be an easy way of getting a list of users in a plain text format to allow running diff. The design of WordPress has two tables, one for users and one for encoded metadata about users of which one will be the access level. The following SQL command will give a list of all users that aren t subscribers (everyone above the minimum level of access which is typical for new users) along with their encoded password and access level. This could be used in a monitoring system to alert about new privileged users. The TABLE_PREFIX variable is for the prefix for WordPress tables, which is wp_ by default but can be any legal value.

   select $TABLE_PREFIXusers.user_login, $TABLE_PREFIXusers.user_pass, $TABLE_PREFIXusermeta.meta_value from  $TABLE_PREFIXusers join $TABLE_PREFIXusermeta on $TABLE_PREFIXusers.id = $TABLE_PREFIXusermeta.user_id and meta_key='$TABLE_PREFIXcapabilities' and meta_value != 'a:1: s:10:"subscriber";b:1; ';

• [1] https://doc.coker.com.au/
• [2] https://en-au.wordpress.org/download/releases/
• [3] https://jamstack.org/generators/
• [4] https://etbe.coker.com.au/2011/12/31/server-cracked/

Welcome to the July 2026 report from the Reproducible Builds project! In our reports, we try to outline the most important things that we have been up to over the past month. As a quick recap about what problem our project intends to solve, whilst anyone may inspect the source code of free software for malicious flaws, almost all software is distributed to end users as pre-compiled binaries. The motivation behind the reproducible builds effort is to ensure no flaws have been introduced during this compilation process by promising identical results are always generated from a given source, thus allowing multiple third-parties to come to a consensus on whether a build was compromised or not. If you are interested in contributing to the project, please visit the Contribute page on our website. In this month s report, we cover:

1. Tool development
2. Distribution work
3. Three new scholarly papers
4. Patches
5. Misc news

Tool development diffoscope is our in-depth and content-aware diff utility that can locate and diagnose reproducibility issues. This month, Chris Lamb made the following changes, including preparing and uploading versions 324, 325 and 326 to Debian:

• Fix tests to work with zipdetails 4.0008. (#1141359)
• Bump debhelper compatibility level to 13. [ ]
• Update copyright years. [ ]

In addition, Paul Spooren made changes to allow trailing garbage in Gzip files [ ] and Vagrant Cascadian added an external tool reference for the pedump binary to use the mono package under GNU Guix. [ ]
disorderfs is our FUSE-based filesystem that deliberately introduces non-determinism into system calls to reliably flush out reproducibility issues. This month, Christelle Gloor added the option to sort by ctime as returned by the lstat(2) syscall. [ ], which Chris Lamb uploaded whilst bumping the Standards-Version to version 4.7.4 [ ]. Bernhard Wiedemann also updated disorderfs to version 0.7.0 in openSUSE.
Yet again, there were a number of improvements made to our website this month as well. For example, Chris Lamb, by request of Digital Ocean, changed the target of a referral link so that they can manage incoming referrers [ ] and pushed a number of changes to the Tools page [ ].

Distribution work In Debian this month, 32 reviews of Debian packages were added, 26 were updated and a total of 21 were removed this month, adding to our extensive knowledge about identified issues. A number of issue types were added by Chris Lamb, including:

• python_towncrier_build_date [ ][ ]
• log_files_installed_in_package [ ]
• fontforge_varies_by_timezone [ ][ ]

Chris also added a further note for the build_date_in_manpage_generated_by_spf13_cobra issue. [ ]
In addition, there is a new page showing verification rebuilds of OpenWrt APK packages and firmware images, powered by rebuilderd:

Three new scholarly papers Yan Li, Nan Jiang, Qihang Zhou, Shaowen Xu, Yamin Xie and Xiaoqi Jia of the Chinese Academy of Sciences published a paper titled VCAligner: Aligning Source Distribution Versions with Upstream Git Commits to Secure Supply Chain:

We present VCAligner, a content-based alignment methodology that constructs inverted indexes over VCS histories to precisely map released artifacts to their originating commits, independent of fragile version tags. We evaluated VCAligner on a dataset of 2,984 verifiable PyPI packages derived from the 4,000 most-downloaded projects linked to public GitHub upstreams. Our results reveal a critical weakness in conventional tag-based heuristics: while they appear effective on 85% of the dataset, the residual 15% failure rate generates a catastrophic downstream audit workload of over 10.3 million commits. In contrast, VCAligner reduces this burden by two orders of magnitude ( 158 ), bounding the total workload to under 65,000 commits. Furthermore, we provide the large-scale characterization of Packaging Noise, classifying artifact divergence into structural additions (Path Phantoms) and content mutations (Blob Phantoms), thereby isolating the distinct attack surfaces of malicious injection and code tampering.

Jens Dietrich and Spencer Sun from the Victoria University of Wellington together with Tim W. White and Behnaz Hassanshahi from Oracle Inc pre-published their paper No Snake Oil: Verifying Python Package Builds (PDF):

Python has become the default language for interacting with AI, with packages being distributed through registries like the Python Package Index (PyPI). This creates a need to analyse supply chains comprising such packages. One such analysis is to rebuild packages in order to identify compromised builds injecting malware. Independent rebuilds in hardened environments have the added advantage that they can generate and record provenance in order to increase the trustworthiness of packages. Two tools that are designed to automate such rebuilds and run them at scale are macaron and oss-rebuild. We study 12,180 popular releases from PyPI and find that the byte-for-byte equivalence rate is generally low. We analyse the reasons why they produce different wheels, and find that equivalence between the original and rebuilt wheels can often still be established, preserving most of the guarantees users expect from rebuildable releases. We present and evaluate daleq4py, a tool to establish the equivalence of Python wheels through the kernel of a normalisation function that is based on provenance-preserving datalog rules. Experimental results show that daleq4py substantially expands the set of rebuilds that can be accepted as equivalent. Although only 15.4% of macaron rebuilds and 19.1% of oss-rebuild rebuilds are byte-for-byte identical to the published PyPI wheels, daleq4py establishes wheel equivalence for 60.2% and 78.9% of source-equivalent rebuilds, respectively.

Denise Nanni, Julien Malka, Stefano Zacchiroli and Th o Zimmermann from T l com Paris together with Gabriele D Angelo from the University of Bologna pre-published their paper Understanding Build Reproducibility in the F-Droid Ecosystem (PDF), which was accepted at the 2026 ACM Conference on Reproducibility and Replicability:

The security of open source applications benefits considerably from the possibility of rebuilding their source and verifying the output. F-Droid, a prominent distribution for open source Android applications, systematically rebuilds them from source and tests their bitwise reproducibility at app publishing time. However, F-Droid offers no guarantee that app reproducibility will continue to hold in the future. As software ecosystems evolve, reproducibility may degrade, with potential negative consequences for software preservation and security. We present the first empirical study of build reproducibility in the F-Droid app ecosystem. Analyzing historical reproducibility logs, we find that the overall bitwise reproducibility rate has been steadily increasing over time (as new versions of apps are published). We then evaluate how reproducibility holds in time for fixed app versions, by attempting to rebuild 18 904 app versions that F-Droid had previously confirmed bitwise reproducible, published between September 2018 and February 2026, achieving an 83% rebuild success rate, and identify missing dependencies as the dominant cause of failure, accounting for 76% of non-rebuildable cases. Among successfully rebuilt apps, 94% are also bitwise reproducible-i.e., they still yield bitwise identical artifacts upon rebuild. Together, these results show that while bitwise reproducibility largely holds for apps that can be rebuilt, rebuildability itself is highly sensitive to temporal decay.

Patches The Reproducible Builds project detects, dissects and attempts to fix as many currently-unreproducible packages as possible. We endeavour to send all of our patches upstream where applicable or possible. This month, we wrote a large number of such patches, including:

• openSUSE monthly
• Arnout Engelen:
  • ivy
  • pekko-grpc
  • sbt-ivy
  • scala
• Bernhard M. Wiedemann:
  • angelfish
  • cpython
  • efl
  • erlang+ex_doc
  • eww/glib-macros
  • intel-graphics-compiler
  • java-11-openjdk
  • java-17-openjdk
  • java-1_8_0-openjdk
  • java-21-openjdk
  • mono-core
  • nasm/rav1e
  • python-langsmith
  • zathura*
  • zig0.15
• Chris Lamb:
  • #1141505 filed against golang-github-tidwall-wal.
  • #1141506 filed against lightproof.
  • #1141582 filed against libslow5lib.
  • #1141687 filed against siso.
  • #1141841 filed against grout.
  • #1142126 filed against libpsl.
  • #1142492 filed against node-grunt-contrib-internal.
  • #1142495 filed against fontforge.
  • #1142496 filed against spopt.
  • #1142887 filed against go-dlib.
  • #1143147 filed against towncrier.
• Jochen Sprickerhof:
  • #1141412 filed against python-sphinx-chango.
  • #1141553 filed against gasnet.
  • #1141577 filed against yubihsm-connector.
  • #1141663 filed against dh-fortran.
  • #1142000 filed against watcher.
  • #1142001 filed against rakudo.
  • #1142128 filed against kf6-breeze-icons.
  • #1142141 filed against oxygen-icons.
  • #1142256 filed against gnu-apl.
  • #1142513 filed against barvinok.
  • #1143169 filed against ecbuild.

Misc news On our mailing list this month, Colin Winter of Markovian Protocol wrote to our mailing list on the topic of Reproducible verification for retained logs:

Reproducible builds remove trust in the builder: anyone re-derives the same artifact from the same source, byte for byte. The same shape applies one layer over, to a retained record. Most record-keeping regimes (the EU AI Act s Article 12 logging is the current example) require that events be recorded and logs retained, but not that a retained log be verifiable, by a party who was not present, as unaltered and existing when claimed. That leaves an integrity obligation resting on trusting the party being audited.

(Full thread)

Finally, if you are interested in contributing to the Reproducible Builds project, please visit our Contribute page on our website. However, you can get in touch with us via:

• IRC: #reproducible-builds on irc.oftc.net.
• Mastodon: @reproducible_builds@fosstodon.org
• Mailing list: rb-general@lists.reproducible-builds.org

This post is an unpublished review for Subscription Bombing Email under Attack

TL;DR: What a great DebConf! I managed to recharge my Debian batteries, and my talks / BoF sessions all went fine. Already looking forward to DebConf in Japan next year!

DebCamp

The evening before DebCamp started, we had a nice bbq (we taught some locals to call it a braai at an organiser s house and went for a walk around the river as the sun set. It was a very peaceful lead-in to DebCamp.

• I set up and sent out the call for Forky desktop artwork:
  • https://lists.debian.org/msgid-search/910906f3-7ce4-4884-a49f-4b4a5975471b@debian.org
• Had many nice discussions about various Debian topics with all the Debian people around. It s really fun being around people who are natural problem solvers who care deeply about both technical and social issues. At one point Jonas told me Holy shit, these people are motivated! and I appreciate that so much too!

Debian LTS wine

• Most of my DebCamp was dedicated to preparing for my demo and main talk that followed at DebConf.
• Sadly, we had no loopy this year, I just didn t have the time, and the people who stepped up to help last year were either overwhelmed with other issues or couldn t make it. I ll try to make it happen again for next year by kicking it off long before DC27.

View of Santa Fe city from hotel

DebConf

Talk Is it even possible to build a truly universal system installer?

In this talk I do a very quick comparison of system installers based on my experience with them. It s hard to directly compare all of them, since there are so many, and each have their own niche that they attempt to satisfy.

I also introduce Yasi my attempt to answer the question of whether we could build a universal installer, which can also better cover advanced installations, automated installations and niche setups.

It s very early days for the project, and I didn t quite feel ready to share the code with the world, but it was nice that I did a quick demo where I could install a Debian system and the resulting system actually booted up. *phew*.

This is also going to be my main focus for the mid-term future. I aim to have all the basic partitioning options working by the time Debian 14 (Forky) is released, and by the time Debian 15 is released, I have a long list of features that I aim to have working. So, my timeline for having something that s generally useful is around a year from now, and in around 3 years it should be a fully fledged installer that should cover a very large amount of Debian use cases and architectures.

Day Trip

For the day trip, we did a tour across Santa Fe, visited Constituci n de la Naci n Argentina, had lunch where we tried various dishes based on local fish from the river, and then went on a boat ride on the river.

BoF Sessions:

Funding in Free Software Projects: I initially registered this BoF because I m increasingly concerned about how upstreams are asking for donations in their software. I increased the scope to talk about funding in free software in general. It followed Marga s talk about funding, which focussed more about how developers are funded in general. We didn t dive very deep into this, but we certainly need some further discussion (and action) on this within Debian.

Debian Social Team: My most important issue for this team is a carry-over from last year, I want to set up barman (packaged in Debian) for live postgres syncing for our larger databases. For the smaller DBs, doing a daily dump is quite cheap. But for Matrix, it s very expensive in terms if i/o and CPU, so it would be ideal to do less regular complete dumps and use live replication for the first line of redundancy instead.

Images Team: I wasn t initially planning to say much during this session, I have some ideas to reduce both size and count of images, without losing any benefits, but I don t have any work to show for that yet. I ended up talking a lot more than I anticipated, the topics covered were quite good and representative of the current state of Debian images built. I don t have time to create a full summary, so I suggest checking the etherpad / video recording if you re interested.

Some more wine variety during the conference dinner

Debianites in the main hacklab

Rosario

I m spending two days in Rosario before I head home. Exploring a bit, catching up with sleep, finishing this blog post, signing keys and exploring some ideas I made note of during DebConf.

Thank you to the DebConf26 Team!

It was a little surreal not being part of any DebConf team for the first time ever, I ve just been too focussed on getting Yasi ready for my talk (no regrets!). I hope to be more involved again next year, in the meantime, I m very grateful to everyone who has made this happen, you did a stellar job! I hope to see many of you again next year in Japan!

Changes in RcppArmadillo version 15.4.2-1 (2026-07-25)

• Upgraded to Armadillo release 15.4.2 (Medium Roast Agave)
  • Fix speed regressions in diagvec() and diagmat()

Changes in RcppArmadillo version 15.4.1-1 [github-only] (2026-07-09)

• Upgraded to Armadillo release 15.4.1 (Medium Roast Agave)
  • Fix for rare infinite recursion bug in sparse version of diagmat()
  • More efficient checks for aliasing

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can sponsor me at GitHub.

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